JP5418363B2 - Shipment planning device, shipment planning method, and computer program - Google Patents

Description

  The present invention relates to a shipping schedule planning device, a shipping schedule planning method, and a computer program, and is particularly suitable for use in planning a shipping schedule for shipping products from a warehouse.

When a large variety of products stored in a warehouse are shipped (shipped), it is important to formulate a shipping plan in order to ship those products efficiently before the due date.
Therefore, in Patent Document 1, an optimization calculation that minimizes an evaluation function defined by a weighted average sum of a delay in shipping date of each product and a transportation cost of each product is performed, and a shipping plan is determined from the result. I am planning.
In Patent Document 2, the product list in which the product name, quantity, scheduled production date, unloading port, and delivery date are sorted is sorted according to the unloading port and the delivery date, and the products are assigned to the ships in the sorted order. I have to.

JP 2005-145573 A JP 2004-131193 A

By the way, at the time of shipping work, a transport device (such as a crane) for transporting a product is used. Therefore, when work concentrates on a specific transport device, product shipping work by the transport device is delayed. In this case, for example, a ship that transports the product must wait until the product is shipped from the warehouse, which may increase the berthing time. Therefore, it is important to formulate a shipping plan so that the work amount of each transporting device that performs the shipping work is leveled as much as possible.
However, in the above-described conventional technology, the work amount of each transport device that performs shipping work is not considered. Therefore, the above-described conventional technique has a problem that it is difficult to make a shipping plan in consideration of the work amount of each transport device that performs the shipping work.

  The present invention has been made in view of the above-described problems, and considers the work load of the transporting device that performs the shipping work in addition to the product deadline and the load of the product transportation means (for example, a ship). The purpose is to make it possible to make a shipping plan.

The shipping plan planning device of the present invention is a shipping plan planning device for planning a shipping amount for each shipping date of a plurality of products shipped from a warehouse using a plurality of conveying devices, the shipping amount of the product, Order information acquiring means for acquiring order information including the shipping deadline date of the product, and transport for acquiring a transport device work occurrence probability that is the number of times of use per unit product of the transport device used for shipping the product The value obtained by multiplying the device work occurrence probability acquisition means and the shipment amount of each product by the shipment date by the transfer device work occurrence probability related to the product is a transfer device capacity upper limit value set for each transfer device and each shipment date. A constraint equation setting means for setting a constraint equation for each shipment date that represents the following, and a step function that represents a target shipment amount of the product that is set based on the order information, wherein the shipment date is a shipment deadline date 0 until There, the ship date is shipping due date and value of the step function becomes a target shipments, the value of the evaluation for the shipping deadline violations amount represented by the difference between the delivery amount of the product and transports the product transport and the maximum load of the unit, the weight coefficient representing a balance between the value of the evaluation for an empty payload represented by the difference between the load capacity of the product to which the vehicle is loaded, the value of the evaluation for the previous Kisora payload , A weighting factor obtaining unit that obtains a weighting factor representing a balance with an evaluation value for the shipping deadline date violation amount, and the shipping deadline for the product using the weighting factor obtained by the weighting factor obtaining unit and day offense against the evaluation function setting means for setting an evaluation function expressed by-out sum weighted with empty load of the transport means for transporting the products, the range that satisfies the constraints imposed by the ship daily constraint The value of the evaluation function And optimization means for optimizing calculation by the MIP to small, the optimization calculation by said optimization means result, when the value of the evaluation function is minimized in a range that satisfies the constraint by the constraint equation And display means for displaying on the display device information relating to the shipping plan including the shipping amount of each product by shipping date.

The shipping plan planning method of the present invention is a shipping plan planning method for planning a shipping amount for each shipping date of a plurality of products shipped from a warehouse using a plurality of conveying devices, the shipping amount of the product, Order information acquisition step for acquiring order information including the shipping deadline date of the product, and transfer for acquiring a transfer device work occurrence probability that is the number of times used per unit product of the transfer device used for shipping the product The value obtained by multiplying the shipment amount by the device work occurrence probability and the shipment amount by the shipment date of the product by the transfer device work occurrence probability for the product is a transfer device capacity upper limit value set by the transfer device and the shipment date. a constraint equation setting step of setting a shipping date by constraint indicating that this is less, a step function which represents the target delivery amount of the product to be set based on the order information, the shipping date shipped due date Until that is 0, the date of shipment is shipment due date and value of the step function becomes a target shipments, the value of the evaluation for the shipping deadline violations amount represented by the difference between the delivery amount of the product, the a maximum load capacity of the vehicle for transporting the products, a weight coefficient representing a balance between the value of the evaluation for an empty payload represented by the difference between the load capacity of the product to which the vehicle is loaded, before Kisora payload A weighting factor obtaining step for obtaining a weighting factor representing a balance between an evaluation value for the shipping deadline date violation amount and a value for the evaluation, and using the weighting factor obtained by the weighting factor obtaining step , said shipping deadline violations amount for the product, and the evaluation function setting step of setting an evaluation function expressed by-out sum weighted with empty load of the transport means for transporting the products, limited by the ship Daily constraint Meet And optimization step of performing optimization calculation by the MIP to minimize the value of the evaluation function in circumference, the optimization calculation by the optimization step results of the evaluation function in the range satisfying the constraint by the constraint equation And a display step of displaying on the display device information relating to a shipping plan including a shipping amount for each shipping date of the product when the value is minimized.

A computer program according to the present invention is a computer program for causing a computer to plan a shipment amount for each shipment date of a plurality of products shipped from a warehouse using a plurality of transfer devices, Order information acquisition step for acquiring order information including a shipment amount and a shipping deadline date of the product, and a transfer device work occurrence probability which is the number of times used per unit product of the transfer device used to ship the product The transfer device work occurrence probability acquisition step for acquiring the product, and the value obtained by multiplying the shipment amount by shipment date of the product by the transfer device work occurrence probability for the product is set for each transfer device and each shipment date. a constraint equation setting step of setting a shipping date by constraint indicating that less equipment capacity upper limit value, the target shipments of the product to be set on the basis of the order information A step function representing, until shipping date is shipment due date is 0, the table by the difference between the date of shipment is shipment due date and value of the step function becomes the target delivery amount, the shipping amount of the product Of the evaluation value for the shipping deadline violated amount, the evaluation value for the empty load amount expressed by the difference between the maximum load amount of the transportation means transporting the product and the load amount of the product loaded on the transportation means The value of the evaluation with respect to the empty load expressed by the difference between the weighting coefficient representing the balance between the maximum load capacity of the transport means for transporting the product and the load capacity of the product loaded on the transport means. A weighting factor acquisition step for obtaining a weighting factor representing a balance with an evaluation value for the deadline date violation amount, and the shipping deadline date violation amount for the product using the weighting factor acquired by the weighting factor acquisition step And this Minimum and the evaluation function setting step of setting an evaluation function expressed by-out weighted sum of the empty load of the transport means for transporting the products, the value of the evaluation function in the range satisfying the constraints imposed by the ship Daily constraint An optimization step for performing an optimization calculation by a mixed integer programming method, and as a result of the optimization calculation by the optimization step, when the value of the evaluation function is minimized within a range satisfying the constraint by the constraint equation, A display step of performing a process for causing the display device to display information relating to a shipping plan including a shipping amount of each product by shipping date is executed.

According to the present invention, a ship due date violation amount, a sum-out weighted with empty payload as a constraint for the evaluation function to minimize, to the shipping date different shipments of a product, the transport equipment work occurrence related to the product Added a constraint that the value multiplied by the probability is less than or equal to the upper limit value of the transport device capacity. As a result, it is possible to devise a shipping plan in consideration of the work deadline of the product and the load amount of the product transportation means, as well as the work amount of the transfer device that performs the shipping work.

It is a figure which shows the 1st Embodiment of this invention and shows an example of the hardware constitutions of a shipping plan planning apparatus. It is a figure which shows the 1st Embodiment of this invention and shows an example of a functional structure of a shipping plan planning apparatus. It is a figure which shows the 1st Embodiment of this invention and shows an example of order information. It is a figure which shows the 1st Embodiment of this invention and shows an example of conveyance performance data. It is a figure which shows the 1st Embodiment of this invention and illustrates an example of the method of classifying each order (product) which concerns on order information into a shipment group. It is a figure which shows the 1st Embodiment of this invention and shows an example of the conveyance performance data of each conveyance apparatus according to a shipment group. It is a figure which shows the 1st Embodiment of this invention and shows an example of the conveyance apparatus work generation | occurrence | production probability according to a shipment group. It is a figure which shows the 1st Embodiment of this invention and shows an example of a shipment financial resource matrix. It is a figure which shows the 1st Embodiment of this invention and shows an example of conveyance apparatus capability upper limit. It is a figure which shows the 1st Embodiment of this invention and shows an example of a ship lot. It is a figure which shows the 1st Embodiment of this invention and shows an example of the loading availability condition. It is a figure which shows the 1st Embodiment of this invention and shows an example of a weighting coefficient. It is a figure which shows the 1st Embodiment of this invention and shows an example of the relationship between target shipment amount and shipment amount. It is a figure which shows the 1st Embodiment of this invention and shows an example of the result of optimization calculation. It is a figure which shows the 1st Embodiment of this invention and shows an example of the shipment amount (shipment plan) from the warehouse classified by shipment group and shipment date. It is a figure which shows the 1st Embodiment of this invention and shows an example of the number of phase products by ship and ship date. It is a figure which shows the 1st Embodiment of this invention and shows an example of the loading capacity according to a shipping date and every ship. It is a figure which shows the 1st Embodiment of this invention and shows an example of the work amount of the conveyance apparatus according to a shipment group and a shipping date. It is a flowchart which shows the 1st Embodiment of this invention and demonstrates an example of the operation | movement process of a shipping plan planning apparatus. It is a figure which shows the 2nd Embodiment of this invention and shows an example of the relationship between target shipment amount and shipment amount. It is a figure which shows the 2nd Embodiment of this invention and shows an example of the result of optimization calculation. It is a figure which shows embodiment of this invention and shows an example of a structure of the warehouse where the conveying apparatus is arrange | positioned. It is a figure which shows embodiment of this invention and illustrates an example of goods warehousing operation | work and shipping operation | work. It is a figure which shows the 3rd Embodiment of this invention and shows an example of order information. It is a figure which shows the 3rd Embodiment of this invention and shows an example of the processing amount of each conveyance apparatus according to a process date reference | standard shipping date. It is a figure which shows the 3rd Embodiment of this invention and shows an example of the conveyance apparatus processing probability of each conveyance apparatus according to a process date reference | standard shipping date. It is a figure which shows the 3rd Embodiment of this invention and shows an example of the result of optimization calculation. It is a figure which shows the 3rd Embodiment of this invention and shows an example of the shipment amount (shipment plan) from the warehouse classified by shipment group and shipment date. It is a figure which shows the 3rd Embodiment of this invention and shows an example of the number of phase products by ship and ship date. It is a figure which shows the 3rd Embodiment of this invention and shows an example of the loading capacity according to a shipping date and a ship. It is a figure which shows the 3rd Embodiment of this invention and shows an example of the work amount of the conveying apparatus classified by shipment group and processing date. It is a figure which shows the 4th Embodiment of this invention and shows an example of a functional structure of a shipping plan planning apparatus. It is a figure which shows the 4th Embodiment of this invention and shows an example of firm order information. It is a figure which shows the 4th Embodiment of this invention and shows an example of a firm order matrix. It is a figure which shows the 4th Embodiment of this invention and shows an example of the conveyance apparatus load generation amount of each conveyance apparatus according to a shipping scheduled date. It is a figure which shows the 4th Embodiment of this invention and shows an example of the conveyance apparatus load generation amount according to a process day. It is a figure which shows the 4th Embodiment of this invention and shows an example of the conveyance apparatus capability net upper limit according to a processing day. It is a figure which shows the 4th Embodiment of this invention and shows an example of the result of optimization calculation. It is a figure which shows the 4th Embodiment of this invention and shows an example of the shipment amount (shipment plan) from the warehouse classified by shipment group and shipment date. It is a figure which shows the 4th Embodiment of this invention and shows an example of the number of phase products by ship and shipment date. It is a figure which shows the 4th Embodiment of this invention and shows an example of the loading capacity according to shipping date and every ship. It is a figure which shows the 4th Embodiment of this invention and shows an example of the work amount of the conveying apparatus classified by shipment group and processing day.

[First Embodiment]
Hereinafter, a first embodiment of the present invention will be described with reference to the drawings. In the present embodiment, a case where a product (for example, a steel plate) in a warehouse is shipped using a transport device such as a crane and the product shipped from the warehouse is transported by ship will be described as an example.

<Structure of warehouse>
FIG. 22 is a diagram illustrating an example of a configuration of a warehouse in which transfer devices are arranged. In the present embodiment, a case will be described as an example in which the amount of work of the transfer device when the transfer device arranged in a warehouse as illustrated in FIG. 22 transfers a steel plate is predicted.
In FIG. 22, the product warehouse is composed of two warehouses 10a and 10b. The warehouse 10a is composed of two buildings (buildings 1 and 2), and each building is divided into four areas represented by addresses 1 to 4. On the other hand, the warehouse 10b is composed of one building (building 3) divided into two areas represented by addresses 1 and 2.
In the warehouses 10 a and 10 b, a track 20 is laid, and freight cars 40 a and 40 b move on the track 20. In addition, although the freight cars 40a and 40b are different freight cars, the range which can be traveled and a conveyance capability are the same. In the warehouse 10a, a cart 50 for transferring products from one building to the other building is arranged.

The product manufactured at the factory is received at address 1 of the ridge 1 of the warehouse 10a. The received product is transported by the crane 30a of the warehouse 10a and is arranged at a predetermined location (for example, the second address of the ridge 1 of the warehouse 10a). At that time, as shown in FIG. 23A, the product 70 is arranged on the top of the product pile 71 where a plurality of products are stacked. Of course, when no product has been arranged yet, there is no product pile 71, so the product 70 conveyed by the crane 30 is arranged at the bottom of the arrangement area. In addition, when the product pile 71 reaches a certain height, another product pile is created.
When the product 70 transported by the crane 30a is arranged at a place other than the ridge 1 of the warehouse 10a, the product 70 is transported using at least one of the freight cars 40a and 40b and the carriage 50. If the crane 30c or the ridge 3 is used, either the crane 30d or 30e is used to place the product 70 at a predetermined location.

At the time of shipment (shipment), as a general rule, as shown in FIG. 23B, the product 70 at the top of the product pile 71 is taken out using the crane 30 (however, below the product pile 71). May take out the product in the direction). When sea transport is designated as the transport mode, the taken out product 70 is transported to the shipping quay by the freight car 40. The product 70 is then shipped and transported to the destination. On the other hand, when land transport is designated as the transport mode, the product 70 is loaded on the truck 60 at the first address of the warehouse 10b and is transported to the relay point. Thereafter, the product 70 is transported to the destination.
In the following description, for the sake of simplicity, a case will be described as an example in which there are only two cranes, the crane 1 and the crane 2, which are considered in the shipping plan creation.

<Hardware configuration of shipment planning device>
FIG. 1 is a diagram illustrating an example of a hardware configuration of the shipping plan planning apparatus 100.
As shown in FIG. 1, a shipping planning apparatus 100 includes a CPU (Central Processing Unit) 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, a PD (Pointing Device) 104, and an HD. (Hard Disk) 105, display device 106, speaker 107, communication I / F (Interface) 108, and system bus 109.

The CPU 101 controls the overall operation of the shipping plan planning apparatus 100 and controls each component (102 to 108) of the shipping plan planning apparatus 100 via the system bus 109.
The ROM 102 stores a basic input / output system (BIOS) and an operating system program (OS) that are control programs for the CPU 101, programs necessary for the CPU 101 to execute processing according to flowcharts described later, and the like.

The RAM 103 functions as a main memory, work area, and the like for the CPU 101. When executing the processing, the CPU 101 loads various computer programs and the like from the ROM 102 and necessary information from the HD 105 into the RAM 103, and executes various processes by executing the computer programs and the information. .
The PD 104 includes, for example, a mouse, a keyboard, and the like, and constitutes an operation input unit for an operator to input an operation to the shipping plan making apparatus 100 as necessary.
The HD 105 constitutes storage means for storing various information, data, files, and the like.
The display device 106 constitutes display means for displaying various information and images based on the control of the CPU 101.
The speaker 107 constitutes an audio output unit that outputs audio related to various types of information based on the control of the CPU 101.

The communication I / F 108 communicates various information and the like with an external device via a network based on the control of the CPU 101.
A system bus 109 is a bus for connecting the CPU 101, ROM 102, RAM 103, PD 104, HD 105, display device 106, speaker 107, and communication I / F 108 so that they can communicate with each other.

<Functional configuration of shipping planning device>
FIG. 2 is a diagram illustrating an example of a functional configuration of the shipping plan planning apparatus 100.
As shown in FIG. 2, the shipping plan planning apparatus 100 includes, as its functions, an order information acquisition unit 201, an order information storage unit 202, a transfer record data acquisition unit 203, a transfer record data storage unit 204, and a shipment group definition logic acquisition unit. 205, shipping group definition logic storage unit 206, shipping group determination unit 207, shipping group storage unit 208, transfer device work probability calculation unit 209, transfer device work probability storage unit 210, shipping resource matrix generation unit 211, shipping resource matrix storage unit 212, a constraint condition acquisition unit 213, a constraint condition storage unit 214, a constraint expression setting unit 215, a planning policy acquisition unit 216, a planning policy storage unit 217, an evaluation function setting unit 218, an optimization unit 219, and a shipping plan output unit 220 Have.

<< Order information >>
The order information acquisition unit 201 acquires order information indicating an order (product) attribute based on an operation input of the PD 104 by an operator or communication via a network with an external device, and stores the order information in the order information storage unit 202. .
FIG. 3 is a diagram illustrating an example of the order information 300 and 301. Specifically, FIG. 3A shows an example of order information 300 of an already manufactured / shipped order, and FIG. 3B shows an example of order information 301 of an order to be shipped. In FIG. 3A and FIG. 3B, for the convenience of explanation, the contents of the order attribute are the same except for the product number, but these are usually different.
In the example illustrated in FIG. 3, the order information 300 and 301 includes information on product number, size, weight, shipping deadline date, transportation form, destination, export country, and customer presence.

Here, the product number is a number for identifying an order (product), and is unique for each product. The size is the size of the product, and here, the size of the product is indicated by the thickness, width, and length. The weight is the weight of the product.
The shipping deadline is the date when the product is shipped (shipped) from the warehouse. Here, the date of planning the shipping plan is represented by “0”, and the date Z (Z is a natural number) after the planning date of the shipping plan is represented by “Z”.
The transport form is information indicating in what form the product is transported. In this embodiment, the case where the product is transported by ship will be described as an example. However, when the product is transported by land, “land transport” is designated as the transport mode.
The destination is information indicating the shipping destination of the product. In this embodiment, the product destination (shipping destination) refers to a place where the product is first dropped after being shipped from the warehouse. In the present embodiment, a case where products are transported by ship will be described as an example, and a port with a shipping quay is a destination. In addition, when the transportation form is land transportation, a destination such as a transit destination of a truck loaded with products at the delivery position of the warehouse is a destination. However, the destination is not necessarily limited to such information as long as it is information related to the shipping destination of the product.

The exporting country is information indicating the country where the product is finally delivered. When the product is not exported, “domestic” is designated as the exporting country information as shown in FIG. As the information of “exporting country”, information indicating “not applicable” may be given instead of the information of “domestic”.
The customer presence is information indicating whether or not the customer is present in the product confirmation work before the product is shipped from the warehouse.
The order information acquisition unit 201 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108). Further, the order information storage unit 202 is realized by using the HD 105, for example.
In the present embodiment, for example, an order information acquisition unit is realized by the processing of the order information acquisition unit 201 as described above.

<< Conveyance data >>
The transport record data acquisition unit 203 receives transport record data from the transport device management apparatus 230 that manages the operation of the transport device (crane, freight car, cart, etc.) that transports products during shipping operations in a warehouse. And stored in the conveyance result data storage unit 204. In the present embodiment, the conveyance result data is data indicating how many times each conveyance device has been used from when the product is received into the warehouse until it is shipped. Further, the transport record data acquisition unit 203 acquires transport record data for all products related to the order information 300 (order attributes of orders already manufactured / shipped) acquired by the order information acquisition unit 201.

FIG. 4 is a diagram illustrating an example of the conveyance result data 400. In the following, in order to simplify the explanation, a case where two cranes are transfer devices will be described as an example. However, in actuality, the number of cranes may be more than two, and cranes In addition to or in place of the crane, a transport device other than the crane may be used.
In FIG. It shows that the first crane (crane 1) is used once and the second crane (crane 2) is used once during the period from the arrival of one product into the warehouse until it is shipped. . The numbers described in the other columns have the same meaning.
The conveyance result data acquisition unit 203 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the communication I / F 108. Further, the transport result data storage unit 204 is realized by using the HD 105, for example.
Here, the case where the conveyance result data 400 is acquired from the conveyance device management apparatus 230 is described as an example, but it is not always necessary to do so. For example, the shipping plan planning device 100 may have the function of the transport device management device 230. Moreover, you may make it acquire the conveyance performance data 400 based on the operation input of PD104 by an operator.
In the present embodiment, for example, an example of the conveyance result acquisition unit is realized by the processing of the conveyance result data acquisition unit 203 as described above.

<<< shipping group definition logic >>>
The shipping group definition logic acquisition unit 205 acquires the shipping group definition logic based on the operation input of the PD 104 by the operator or communication with the external device via the network, and stores the shipping group definition logic in the shipping group definition logic storage unit 206.
In this embodiment, the shipment group definition logic includes “transportation mode” that is an order attribute that affects at least one of the shipment destination and the delivery position of the product in the warehouse among the orders (products) related to the order information 300. Assume that the logic is to group orders (products) with the same “destination” into one shipping group.
The shipment group definition logic acquisition unit 205 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108). Further, the shipment group definition logic storage unit 206 is realized by using, for example, the HD 105.

<< Conveyance equipment workload prediction model >>
(Delivery of shipping group)
The shipment group discriminating unit 207 follows the order information 301 (order attribute of the order to be shipped, FIG. 3 ()) stored in the order information storage unit 202 according to the shipment group definition logic stored in the shipment group definition logic storage unit 206. Each order (product) related to (see b)) is classified into a shipping group and stored in the shipping group storage unit 208.
FIG. 5 is a diagram conceptually illustrating an example of a method for classifying each order (product) related to the order information 300 into a shipping group.
First, the shipping group determination unit 207 refers to the order information 300 and collects orders (products 70) having the same “transportation mode” and “destination” into one. For example, as shown in FIG. 2 (product) and product no. In “Order 3”, the “transportation mode” is “sea transport” and the “destination” is “port A”. Therefore, the shipping group determination unit 207 combines these orders (products 70) into one. In this way, a shipping group as shown in the third column of FIG. 5 is obtained. The shipping group determination unit 207 stores the shipping group classification result in the shipping group storage unit 208.
The shipping group determination unit 207 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108). Further, the shipment group storage unit 208 is realized by using, for example, the HD 105.
In the present embodiment, for example, an example of a shipping group generation unit is realized by the processing of the shipping group determination unit 207 as described above.

(Calculation of transfer results data for each transfer device for each shipment group)
The transfer device work probability calculation unit 209 reads the order information stored in the order information storage unit 202 (order attributes of orders already manufactured / shipped, see FIG. 3A) and stores the shipment group definition logic. The shipment group definition logic stored in the unit 206 is read out. The transfer device work probability calculation unit 209 classifies each order (product) in the order information into a shipping group according to the shipping group definition logic. The classification method to the shipping group is as described above. As a result, product no. 2, 3, 8 orders, product no. 1 and 7 orders, product no. 5, 9, 10 orders, product no. The orders 4 and 6 are classified into shipping groups A, B, C, and D, respectively. Based on the shipping group information obtained in this way and the transport record data 400 stored in the transport record data storage unit 204, the transport device work probability calculation unit 209 determines each transport device for each shipment group. Transport result data (total value of the number of times each transport device is used for each shipment group) is calculated and temporarily stored in the RAM 103 or the like.
FIG. 6 is a diagram illustrating an example of the transfer result data 600 of each transfer device by shipment group.
Here, the “manufactured / shipped order quantity (product quantity)” belonging to the shipping group A is “15”, and the “manufactured / shipped order quantity (product quantity)” belonging to the shipping groups B to D respectively. It is assumed that “10”. In this case, the transfer device work probability calculation unit 209 writes “15” in the “number of products” field of “shipping group A” in FIG. 6 and “number of products” in the “number of products” column of “shipping groups B to D”. 10 "is written.

In the example shown in FIG. 4, the total number of times of use of the first crane (crane 1) in the “manufactured / shipped order (product)” belonging to the shipping group A is “12”. Assume that the total value of the number of times the second crane (crane 2) is used in “manufactured / shipped orders (products)” belonging to group A is “3”. In this case, the transfer device work probability calculation unit 209 writes “12” and “3” in the “crane 1” and “crane 2” fields of “shipment group A”, respectively.
Similarly, the transfer device work probability calculation unit 209 sets “2” and “8” in the “crane 1” and “crane 2” fields of “shipping group B” and “crane 1” of “shipping group C”, respectively. "8" and "2" are respectively written in the "crane 2" and "2" and "8" are written in the "crane 1" and "crane 2" fields of the "shipping group D", respectively.

(Calculation of transport equipment work occurrence probability for each shipping group)
The transfer device work probability calculation unit 209 calculates the transfer device work occurrence probability for each shipment group based on the transfer result data 600 of each transfer device for each shipment group obtained as described above, and transfers the transfer device work probability. Store in the probability storage unit 210. In the present embodiment, the transport device work occurrence probability is the number of times that each transport device is used in each shipping group by the number of “manufactured / shipped orders (products)” belonging to the shipping group (the number of products in FIG. 6). Divided value. That is, the conveyance device work occurrence probability is the number of times each conveyance device is used in each shipping group per unit order (unit product).
FIG. 7 is a diagram illustrating an example of the transfer equipment work occurrence probability 700 for each shipment group.
In FIG. 7, the number of uses of the first crane (crane 1) in the shipping group A is “12”. The number of orders (products) belonging to the shipping group A (number of products) is “15”. Therefore, the transfer device work probability calculation unit 209 sets the number of uses “12” of the first crane (crane 1) in the shipping group A and the number of orders (products) belonging to the shipping group A (the number of products) to “15”. To obtain “0.8” as the work occurrence probability of the first crane (crane 1) in the shipping group A.
The values in the other columns in FIG. 7 are also obtained by performing the same calculation as the above calculation.

The transfer device work probability calculation unit 209 stores the transfer device work occurrence probability 700 for each shipment group calculated as described above in the transfer device work probability storage unit 210 as a transfer device work amount prediction model.
The transport apparatus work probability calculation unit 209 is realized by using, for example, the CPU 101, the ROM 102, and the RAM 103. Further, the transfer device work probability storage unit 210 is realized by using, for example, the HD 105.
In the present embodiment, for example, the processing of the transport device work probability calculation unit 209 as described above realizes an example of the transport device work occurrence probability acquisition unit.

<Shipping financial resources matrix>
The shipping resource matrix generation unit 211 is stored in the order information 301 stored in the order information storage unit 202 (order attributes of orders scheduled to be shipped, see FIG. 3B) and the shipping group storage unit 208. Based on the shipping group classification result (see FIG. 5), a shipping resource matrix is generated and stored in the shipping resource matrix storage unit 212.
FIG. 8 is a diagram illustrating an example of the shipping resource matrix 800.
In FIG. 8, a shipping funding matrix 800 represents the order quantity by shipping group and by shipping deadline date. In the example shown in FIG. 8, the order quantity is represented by the product weight [ton]. Further, here, when the “shipment due date” is the day of the planning date of the shipping plan, the “shipping date” is set to “0”, and the “shipping date” is the shipping plan planning date Z (Z is If it is a natural number) days later, the “delivery date” is set to “Z”.
The shipping resource matrix generation unit 211 is realized by using, for example, the CPU 101, the ROM 102, and the RAM 103. Further, the shipping resource matrix storage unit 212 is realized by using, for example, the HD 105.
In the present embodiment, for example, an example of the shipping resource matrix generation unit is realized by the processing of the shipping resource matrix generation unit 211 as described above.

<Restrictions>
The constraint condition acquisition unit 213 acquires the constraint condition setting value based on the operation input of the PD 104 by the operator or communication with the external device via the network, and stores it in the constraint condition storage unit 214. The constraint condition setting value is a constant in a constraint equation used to calculate the shipment amount (loadAmount _{i, j} ) for each shipment group and each shipment date, as will be described later. In the present embodiment, the following are set as the constraint condition setting values.
Transport equipment capacity upper limit (craneMax _{m, j} )
Maximum number of ships per day (MaxDayShip)
Ship lot (shipLotSize _ig )
Loading condition (MixCondition _{ig, i} )
Shipment amount upper limit (MaxloadAmount _j )
Shipment flag set value (M)

The transport device capacity upper limit value (craneMax _{m, j} ) is an upper limit value of the work amount of the transport device m on the shipping date j. Here, the work amount is represented by the weight [ton] of the product transported by the crane. FIG. 9 is a diagram illustrating an example of the transport device capability upper limit value 900.
The daily maximum number of ships dispatched (MaxDayShip) is the upper limit of the number of ships dispatched by shipping date.
The ship lot (shipLotSize _ig ) is the maximum load capacity [ton] of the ship ig. FIG. 10 is a diagram showing an example of the ship lot 1000. As shown in FIG.
The loading condition (MixCondition _{ig, i} ) is “1” when the order (product) of the shipping group i can be loaded on the ship ig, and “0” when the loading is impossible. . FIG. 11 is a diagram illustrating an example of the loading permission / inhibition condition 1100.
The shipment amount upper limit value (MaxloadAmount _j ) is an upper limit value of the shipment amount of the product on the shipment date j. Here, it is assumed that the shipment amount is represented by the product weight [ton].
The shipment flag set value (M) is for setting a shipment flag loadD _{i, j} described later to “1” when products belonging to each shipment group i are shipped on the shipment date j. As will be described later, a sufficiently large positive value is set as the shipping flag set value (M).
The constraint condition acquisition unit 213 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108). Further, the constraint condition storage unit 214 is realized by using the HD 105, for example.

<Constraint expression>
The constraint equation setting unit 215 stores the shipping resource matrix 800 stored in the shipping resource matrix storage unit 212, the transport device work occurrence probability 700 stored in the transport device work probability storage unit 210, and the constraint condition storage unit 214. Using the stored constraint condition setting values, a constraint equation for calculating the shipment amount (loadAmount _{i, j} ) for each shipment group and each shipment date is set as described later.
In the present embodiment, the constraint equation setting unit 215 sets the following constraint equation.

(Relationship between shipment amount and empty load amount)
The constraint equation setting unit 215 sets the following linear equation constraint equation (1).

The loadAmountGig _{, j on} the left side of the equation (1) is a decision variable (a real number greater than or equal to 0) representing the load amount of the ship ig on the shipping date j. ShipCount _{ig, j} is a decision variable (an integer greater than or equal to 0) representing the number of ships assigned to ship ig on shipment date j. shipLotSize _ig is a ship lot stored in the constraint condition storage unit 214. LoadRatioCover _{ig, j} is a decision variable (a real number greater than or equal to 0) that represents an empty load amount on the shipping date j of the ship ig. The empty load amount is equal to a value obtained by subtracting the load amount of the ship ig from the ship lot of the ship ig.
As described above, the equation (1) indicates that the load amount of the ship ig on the shipment date j is the total value of the maximum load amount of the ship ig allocated on the shipment date j, It means that it must be equal to the value obtained by subtracting the empty load amount of ig.

(All orders shipped)
The constraint equation setting unit 215 sets the following linear equation constraint equation (2).

In the formula (2), the left side is the total shipping amount (planned value) of the shipping group i in the shipping planning period (shipping planning date “0” to “N” N days after the shipping planning date). On the other hand, in the equation (2), the right side is the total order quantity of the shipping group i in the shipping planning period (shipping planning date “0” to “N” N days after the shipping planning date). That is, equation (2) represents that the total shipment quantity in the shipping group i must be equal to the total order quantity in the shipping group i.
Note that loadAmount _{i, j in} equation (2) is a decision variable (a real number greater than or equal to 0) representing the shipment amount of products belonging to the shipment group i on the shipment date j. loadOrderAmount _{i, j} is the order quantity of products belonging to the shipping group i on the shipping date j. The right side of equation (2) is obtained by adding the values of the shipping funding matrix 800 for each shipping group i.

(Relationship of variables related to shipment volume)
The constraint equation setting unit 215 sets the following linear equation constraint equations (3) and (4).

Further, in the expression (3), the left side represents the shipping group i and the shipping deadline for all shipping deadlines r in the shipping planning period (shipping planning date “0” to “N” N days after the shipping planning date). This is the sum of the shipment quantities of products on day r on the shipment date j. On the other hand, the right side is the shipment amount of the product of the shipment group i on the shipment date j.
Some orders (products) belonging to the same shipping group and shipping date have different shipping deadlines. Therefore, in equation (3), the total value of the shipment quantities of “orders (products) belonging to the same shipping group and shipping date” having different shipping deadlines is the same for the orders (products) belonging to the same shipping group and shipping date. It means that it must be equal to the shipment amount.
Note that loadDeadAmount _{i, r, j in} equation (3) is a decision variable (a real number greater than or equal to 0) that represents the shipment amount on the shipment date j of an order (product) whose shipment group is i and whose shipment deadline is r. It is. Also, loadAmount _{i, j} is a decision variable (a real number greater than or equal to 0) representing the shipment amount from the warehouse on the shipment date j of products belonging to the shipment group i.

Further, in the equation (4), the left side indicates the total shipment amount of orders (products) on the shipping date j of the shipping group i that can be loaded on the ship ig among all the shipping groups i (total number of shipping groups = I). It is. On the other hand, in the equation (4), the right side is the loading amount of the ship ig on the shipping date j. That is, in the equation (4), the total shipment amount of orders (products) on the shipping date j of all shipping groups i that can be loaded on the ship ig must be equal to the loading amount of the ship ig on the shipping date j. Represent.
Note that MixCondition _{ig, i in the} equation (4) is a stacking condition, and is stored in the constraint condition storage unit 214. Also, loadAmount _{i, j} is a decision variable (a real number greater than or equal to 0) representing the shipment amount of products belonging to the shipment group i on the shipment date j. Also, loadAmountG _{ig, j} is a decision variable (a real number greater than or equal to 0) representing the load amount of the ship ig on the shipping date j.

(Shipping flag)
The constraint equation setting unit 215 sets the following linear inequality constraint equation (5).

In equation (5), loadAmount _{i, j} is a decision variable (a real number greater than or equal to 0) representing the shipment amount of products belonging to the shipment group i on the shipment date j. M is a shipment flag set value, and is stored in the constraint condition storage unit 214. Also, loadD _{i, j} is a shipping flag, and is a decision variable that is “1” when products belonging to the shipping group i are shipped on the shipping date j, and “0” otherwise.
In the equation (5), the shipment amount loadAmount _i , j of the products belonging to the shipping group i on the shipping date j is “0” or “1” (shipping flag loadD _{i, j} ) in the shipping flag setting value (M). It indicates that it is less than the multiplied value. That is, in the equation (5), when the shipment amount loadAmount _{i, j} on the shipment date j of the product belonging to the shipment group i is a positive value, the shipment flag loadD _{i, j} must be always “1”. It is a constraint for being. Therefore, the shipping flag set value (M) needs to be a positive value sufficiently larger than a value assumed as the shipping amount loadAmount _{i, j} on the shipping date j of the products belonging to the shipping group i.

(Relationship between loading number and shipping flag)
The constraint equation setting unit 215 sets the following linear equation constraint equation (6).

In the equation (6), loadMixNig _{, j on the} left side is a decision variable (an integer greater than or equal to 0) that represents the number of shipping groups i to which products loaded on the ship ig belong on the shipping date j. In the following description, the number of shipping groups to which orders (products) loaded together on one ship belong is referred to as the number of mutual products as necessary. On the other hand, loadD _{i, j on} the right side of the equation (6) is a shipping flag, which is “1” when products belonging to the shipping group i are shipped on the shipping date j, and “0” otherwise. Is a decision variable. Further, MixCondition _{ig, i} is a stacking availability condition and is stored in the constraint storage unit 214 (see FIG. 11). That is, in the equation (6), the number of the shipping group i to which the order (product) loaded on the ship ig on the shipping date j belongs is the order (product) shipped on the shipping date j and loaded on the ship ig. Indicates that it must be equal to the total number of shipment groups i.

(Maximum daily shipping volume)
The constraint equation setting unit 215 sets the following linear inequality constraint equation (7).

The loadAmount _{i, j on} the left side of the equation (7) is a decision variable (a real number greater than or equal to 0) representing the shipment amount of products belonging to the shipment group i on the shipment date j. On the other hand, MaxloadAmount _{j on} the right side of the equation (7) is a shipping amount upper limit value and is stored in the constraint condition storage unit 214. That is, equation (7) represents that the shipment amount of products belonging to all shipment groups i on the shipment date j must be less than or equal to the upper limit of the shipment amount of products on the shipment date j.

(Maximum daily number of ships)
The constraint equation setting unit 215 sets the following linear inequality constraint equation (8).

ShipCount _{ig, j on} the left side of the equation (8) is a decision variable (an integer greater than or equal to 0) that represents the number of ships ig to be dispatched on the shipping date j. On the other hand, MaxDayShip on the right side of equation (8) is the daily maximum number of ships dispatched and is stored in the constraint storage unit 214. That is, equation (8) indicates that the total number of ships ig to be dispatched on the shipping date j must be less than or equal to the daily ship allocation upper limit.

(Upper limit of transfer device capacity)
The constraint equation setting unit 215 sets the following linear inequality constraint equation (9).

The loadAmount _{i, j on} the left side of equation (9) is a decision variable (a real number greater than or equal to 0) that represents the shipment amount of products belonging to the shipment group i on the shipment date j. Also, craneRatio _{i, m} is a transfer device work occurrence probability 700 of the transfer device m in the shipping group i, and is stored in the transfer device work probability storage unit 210. On the other hand, craneMax _{m, j on} the right side of equation (9) is the transport device capacity upper limit value, and is stored in the constraint condition storage unit 214 (see FIG. 9). That is, the expression (9) represents that the amount (weight) that the transport device m transports products belonging to all the shipping groups i on the shipping date j must be equal to or less than the transport device capacity upper limit value. In the example shown in FIG. 9, the transport device capacity upper limit value is a constant value regardless of the shipping date, but a different value may be set for each shipping date.

As described above, the constraint equation setting unit 215 uses the information stored in the constraint condition storage unit 214 (shipLotSize _ig , MixCondhition _{ig, i} , M, M) in the equations (1) and (4) to (9). MixCondition _{ig, i} , MaxloadAmount _j , MaxDayShip, craneMax _{m, j} ). Further, the constraint equation setting unit 215 sets the information (craneRatio _{i, m} ) stored in the transfer device work probability storage unit 210 in equation (9). Further, the constraint equation setting unit 215 sets a value (loadOrderAmount _{i, j} ) obtained by summing the values of the shipment resource matrix 800 stored in the shipment resource matrix storage unit 212 for each shipment group in the equation (2).
The constraint equation setting unit 215 is realized by using, for example, the CPU 101, the ROM 102, and the RAM 103.
As described above, in this embodiment, for example, the constraint equation setting unit 215 sets information on constraint equations such as equation (9), thereby realizing an example of a constraint equation setting unit.

<Planning policy>
The planning policy acquisition unit 216 acquires the planning condition setting value based on the operation input of the PD 104 by the operator or communication with the external device via the network, and stores it in the planning policy storage unit 217. The planning condition set value is a weighting factor for calculating the shipping amount (loadAmount _{i, j} ) for each shipping group and each shipping date as will be described later. In the present embodiment, the following are set as the planning condition setting values.
Weighting factor for shipping deadline violation amount (w ₁ )
Weighting factor for empty load (w ₂ )
Weighting factor for phase product number (w ₃ )
As described above, the number of mutual products means the number of shipping groups to which orders (products) loaded together on one ship belong. FIG. 12 is a diagram illustrating an example of the weighting factor 1200. In FIG. 12, the shipping deadline date violation amount weight corresponds to the weighting factor (w ₁ ) for the shipping deadline date violation amount, and the empty loading amount minimization weight corresponds to the weighting factor (w ₂ ) for the empty loading amount. The minimizing weight corresponds to the weighting factor (w ₃ ) for the number of product.

The planning policy acquisition unit 216 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108). The planning policy storage unit 217 is realized by using the HD 105, for example.
In the present embodiment, for example, an example of a weight coefficient acquisition unit is realized by the processing of the planning policy acquisition unit 216 as described above.

<Evaluation function>
The evaluation function setting unit 218 is stored in the shipping resource matrix 800 stored in the shipping resource matrix storage unit 212, the constraint condition setting value stored in the constraint condition storage unit 214, and the planning policy acquisition unit 216. Set the evaluation function for minimizing the amount of violation of the shipping deadline date, minimizing the empty load, and minimizing the number of cross products within the range satisfying the constraints based on the above-mentioned constraint formulas using the set values of the planning conditions To do.

(Comprehensive evaluation function)
The evaluation function setting unit 218 sets a comprehensive evaluation function of the following expression (10).

In equation (10), J ₁ is a shipping deadline violation amount evaluation value, J ₂ is an empty loading amount evaluation value, and J ₃ is a phase number evaluation value. Further, w ₁ is a weighting factor for the degree of compliance with the shipping deadline date, w ₂ is a weighting factor for the empty load, and w ₃ is a weighting factor for the number of products, each of which is a planning policy storage unit 217. Is remembered. Min represents minimization. As described above, in the present embodiment, the ship due date violations amount evaluation value J _1, is empty stacking amount evaluation value J _2, the sum-out weighted phase product number evaluation value J ₃ so as to minimize .

(Evaluation of compliance with shipping deadline)
The evaluation function setting unit 218 sets the shipping deadline date violation amount evaluation value J ₁ as in the following expressions (11) and (12).

In the equations (11) and (12), loadDeadlineAmount _{i, r, j ′} is a decision variable (representing the shipment amount on the shipment date j ′ of an order (product) whose shipment group is i and whose shipment deadline is r. A real number greater than or equal to zero). Further, loadAmountRef _{i, j} is a cumulative value of the target shipment amount on the shipment date j of the order (product) of the shipment group i, and the target shipment obtained from the shipment resource matrix 800 stored in the shipment resource matrix storage unit 212. Set based on quantity value loadOrderAmount _{i, r} . Thus, in the equation (11), the difference between “shipment amount loadDeadlineAmount _{i, r, j ′} and target shipment amount loadAmountRef _{i, j} ” in all shipping groups i, all shipping deadlines r, and all shipping dates j. To get the absolute value of.

In this embodiment, as shown in the equation (12), the target shipment amount loadAmountRef _{i, j} is “0” until the shipping date j reaches the shipping deadline date r, and the shipping date j is the shipping deadline. It is assumed that the function is a step function having a target shipment amount value loadOrderAmount _{i, r} on day r.
FIG. 13 is a diagram illustrating an example of the relationship between the target shipment amount loadAmountRef _{i, j} and the shipment amount loadDeadlineAmount _{i, r, j ′} .
In FIG. 13, an area 1301 indicated by diagonal lines is a cumulative value of the shipment amount of an order (product) shipped prior to the shipment deadline date r. On the other hand, a region 1302 is a shortage of the cumulative value of the shipping amount with respect to the target shipping amount (cumulative value) loadAmountRef _{i, j} . In formula (10), minimizing the shipping deadline date violation amount evaluation value J ₁ minimizes the area 1301 shown in FIG. 13 (minimizes the shipping volume preceding the shipping deadline date r); This means that the area 1302 is minimized (the difference between the shipment amount after the shipment deadline date r and the target shipment amount is minimized).

(Evaluation of empty load)
The evaluation function setting unit 218 sets the empty load amount evaluation value J ₂ as shown in the following equation (13).

In Expression (13), LoadRatioCover _{ig, j} is an empty load amount on the shipping date j of the ship ig. In equation (13) _, the total value of the empty load amounts LoadRatioCoverig, j for all ships ig and all shipping dates j is obtained. Here, by dividing LoadRatioCoverig _{, j} by (1 + j), weighting is performed so that the effect on the empty load amount becomes smaller as the shipment date j precedes the shipment planning date. When the shipment date j is close to the shipping plan planning date, the ship ig is shipped as much as possible, whereas when the shipping date j is ahead of the shipping planning date, the ship ig This is to allow a sufficient load capacity. The weighting method in the equation (13) is not limited to this. For example, division by (1 + j) ² or division by (1 + j) ^1/2 can be performed. Further, the weighting can be made lighter or heavier according to the shipping date j.
In Equation (10), minimizing the empty load amount evaluation value J ₂ means minimizing the total value of the empty load amounts LoadRatioCover _{ig, j} for all ships ig and all shipment dates j.

(Evaluation of phase product number)
The evaluation function setting unit 218 sets the phase product number evaluation value J ₃ as shown in the following expression (14).

In the equation (14), loadMixNig _{, j} is the number of shipping groups i (number of phase products) to which the products loaded on the ship ig belong on the shipping date j. In the equation (14), the total value of the number of shipping groups i to which the products with which the ship ig belongs is obtained is obtained for all the ships ig and all the shipping dates j.
(10) In the formula, minimizing the phase product number evaluation value J ₃ is, all ships ig, the ship ig is in all shipping date j to minimize the sum of the number of shipments groups i to Aiseki Means.

The evaluation function setting unit 218 uses the weighting factor w ₁ for the shipment deadline violation amount for the shipment deadline violation amount evaluation value J ₁ , the empty loading amount evaluation value J ₂ , and the cross product number evaluation value J ₃ as described above. A weighting factor w ₂ for the empty load amount and a weighting factor w ₃ for the number of phase products are set (see equation (10)). In addition, the evaluation function setting unit 218 calculates the loadAmountRef _{i, j in the} expression (11) based on the target shipment amount value loadOrderAmount _{i, r} obtained from the shipping resource matrix 800 stored in the shipping resource matrix storage unit 212. Set. In addition, the evaluation function setting unit 218 sets the load availability condition MixCondition _{ig, i} stored in the constraint condition storage unit 214 as a variable for determining loadMixNig _{, j in the} equation (14) (see also the equation (6)). .
The evaluation function setting unit 218 is realized by using, for example, the CPU 101, the ROM 102, and the RAM 103.
In the present embodiment, an example of the evaluation function setting unit is realized by the processing of the evaluation function setting unit 218 as described above.

<Optimization calculation>
The optimization unit 219 performs, for example, optimization calculation by a mixed integer programming method, and evaluates within a range that satisfies the constraints based on the constraint equations (equation (1) to (9)) set by the constraint equation setting unit 215. A decision variable that minimizes the evaluation function (equation (10)) set by the function setting unit 218 (such as shipment quantity loadAmount _{i, j} from the warehouse for each shipment group i and each shipment date j) is obtained. In the mixed integer programming, the equation (11) cannot be solved as it is, so the calculation is performed after converting the equation (11) into an expression obtained by removing the absolute value of the equation (11).
The optimization unit 219 is realized by using, for example, the CPU 101, the ROM 102, and the RAM 103.
FIG. 14 is a diagram illustrating an example of a result of optimization calculation performed by the optimization unit 219. In FIG. 14, case 1 is a comparative example, and cases 2 and 3 are those of this embodiment. In case 1, when the “conveyance device constraint” is set to “none”, the constraint equation (9) may not be set. Further, when “impossibility of mutual product” is set to “impossible”, the following constraint equation (15) may be added. Here, Case 1 and Case 2 differ only in “Conveyance device restriction”. In addition, Case 2 and Case 3 differ only in “compatibility”.

In FIG. 14, comparing Case 1 and Case 2, it can be seen that the provision of “conveying device restriction” can prevent the conveying device from conveying an amount exceeding the restriction (“conveying device in FIG. 14”). (See Constraint Violations.)
Further, comparing Case 2 and Case 3, it can be seen that the “empty load” can be reduced from 3464 [ton] to 1464 [ton] by enabling “phase product” (FIG. 14). (See “Empty loading”). Also, since the “number of products” and “empty load” are in a trade-off relationship, the “number of ports near the port (the number of ports where ships will stop for loading)” has changed from “10” to “18”. It has increased. Therefore, it can be seen that the result of the calculation reflects the actual state and operation of the ship (see “Number of Ports Closed” in FIG. 14).
In the present embodiment, for example, an example of an optimization unit is realized by the processing of the optimization unit 219 as described above.

<Output of shipping plan>
The shipment plan output unit 220 displays the shipment amount loadAmount _{i, j} for each shipment group i and each shipment date _j on the display device 106 based on the result of the optimization calculation by the optimization unit 219. In addition to this, in the present embodiment, the shipment plan output unit 220 includes the number of phase loads loadMixN _{ig, j for each} ship ig and each shipment date j, the load amount (loadAmountG _{ig, j} ) for each shipment date j and each ship ig, Information related to the work amount (loadAmount _{i, j} × craneRatio _{i, m} ) of the transfer device for each shipment group i and each shipment date j is displayed on the display device 106. Here, the case where case 3 of FIG. 14 is implemented will be described as an example.

FIG. 15 is a diagram illustrating an example of a shipment amount (shipment plan) from a warehouse for each shipment group i and each shipment date j.
FIG. 15 shows that, for example, the order (product) belonging to the shipping group A is planned to be shipped 648 [ton] on the day when the shipping date is “0” (the day of the shipping planning date).
FIG. 16 is a diagram illustrating an example of the number of phase products for each ship ig and each shipping date j.
In FIG. 16, for example, on the day when the shipping date is “0” (the day of the shipping planning date), the ship 2 does not perform mutual product (number of mutual products = “1”) and nothing is loaded on the ship 1. (The number of phase products = “0”). Also, on the day of shipment date “1”, nothing is loaded on the ship 1 (number of phase products = “0”), and on the ship 2 orders (products) belonging to the three shipping groups are stacked (phased). Product number = “3”).

FIG. 17 is a diagram illustrating an example of the loading amount by ship date j and ship ig.
In FIG. 17, for example, on the day when the shipping date is “1” (the day after the shipping planning date), no order (product) is loaded on the ship 1 (loading amount = 0 [ton]). 2 indicates that an order (product) of 1980 [ton] is loaded.
FIG. 18 is a diagram illustrating an example of the work amount of the transfer device for each shipment group i and each shipment date j. Specifically, FIG. 18A shows an example of the work amount of the crane 1, and FIG. 18B shows an example of the work amount of the crane 2.
In FIG. 18A, for example, on the day when the shipping date is “1” (the day after the shipping planning date), the crane 1 does not carry orders (products) belonging to the shipping group A, and does not ship the shipping group. Order (product) belonging to B is transported by 56 [ton], order (product) belonging to shipping group C is transported by 553 [ton], order (product) belonging to shipping group D is transported by 202 [ton], and the total 811 indicates that an order (product) of 811 [ton] is transported.

In FIG. 18B, for example, on the day when the shipping date is “1” (the day after the shipping planning date), the crane 2 does not carry orders (products) belonging to the shipping group A, An order (product) belonging to the shipping group B is conveyed 224 [ton], an order (product) belonging to the shipping group C is conveyed 138 [ton], and an order (product) belonging to the shipping group D is conveyed 807 [ton] , A total of 1169 [ton] orders (products) are conveyed.
As shown in FIG. 18, on any shipping date, the total value of the work amounts of the cranes 1 and 2 is suppressed to the transport device capacity upper limit 900 (= 1700 [ton]) or less shown in FIG. I understand that.

The shipping plan output unit 220 displays an OK button and an NG button when displaying the information as shown in FIGS. 15 to 18 on the display device 106.
When the OK button is pressed by the operation of the PD 104 by the operator, it is assumed that the information (shipment plan) as shown in FIGS. 15 to 18 is accepted.
On the other hand, when the NG button is pressed by the operation of the PD 104 by the operator, the information (shipment plan) as shown in FIGS. 15 to 18 is not accepted. In this case, the operator changes at least one of the constraint condition setting value and the planning condition setting value to update the storage contents of the constraint condition storage unit 214 and the planning policy storage unit 217. For example, when it is desired to further comply with the shipping deadline date, the weighting factor w ₁ for the shipping deadline date violation amount evaluation value J ₁ , which is one of the planning condition setting values, may be increased. Then, the constraint equation setting unit 215 and the evaluation function setting unit 218 reset the constraint equation and the evaluation function according to the updated contents. The optimization unit 219 performs optimization calculation based on the reset constraint equation and evaluation function. The shipping plan output unit 220 displays again the information as shown in FIGS. 15 to 18 on the display device 106 based on the result of the optimization calculation. Such processing is repeated until the operator presses the OK button.
The shipping plan output unit 220 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, the PD 104, and the display device 106.
In the present embodiment, for example, an example of a display unit is realized by the processing of the shipping plan output unit 220 as described above. In the present embodiment, the information shown in FIG. 15 is an example of information on the shipping amount by product shipping date when the value of the evaluation function is minimized within a range that satisfies the constraint by the constraint equation. The information shown in FIGS. 16 to 18 is an example of information related to other shipping plans.

<Operation Process of Shipment Planning Device 100>
Next, an example of operation processing of the shipping plan planning device 100 will be described with reference to the flowchart of FIG. Here, the order information 300, the transportation record data 400, the shipping group definition logic, the constraint condition setting value, and the planning condition setting value are the order information storage unit 202, the transportation result data storage unit 204, and the shipping group definition logic storage unit 206, respectively. The description will be made assuming that the constraint condition storage unit 214 and the planning policy storage unit 217 are already stored.

First, in step S 1901, the shipping group determination unit 207 determines the order information 301 (order of shipping scheduled) stored in the order information storage unit 202 according to the shipping group definition logic stored in the shipping group definition logic storage unit 206. Each order (product) related to the order attribute (see FIG. 3B) is classified into a shipping group, and the result is stored in the shipping group storage unit 208 (see the first and third columns in FIG. 5). .
Next, in step S1902, the shipping resource matrix generation unit 211 orders the order information 301 stored in the order information storage unit 202 and the shipping group classification result stored in the shipping group storage unit 208 (see FIG. 5). ) To generate a shipping financial resource matrix 800 and store it in the shipping financial resource matrix storage unit 212 (see FIG. 8).

Next, in step S1903, the transfer device work probability calculation unit 209 determines each item of the order information stored in the order information storage unit 202 (order attributes of orders already manufactured / shipped, see FIG. 3A). The orders (products) are classified according to the shipping group definition logic stored in the shipping group definition logic storage unit 206. Then, the transfer device work probability calculation unit 209 uses the shipment group information obtained by such classification and the transfer record data 400 stored in the transfer record data storage unit 204 to determine each shipment group. Transport result data 600 of the transport device is calculated (see FIG. 6).
Next, in step S1904, the transfer device work probability calculation unit 209 calculates a transfer device work occurrence probability 700 for each shipment group based on the transfer result data 600 of each transfer device for each shipment group, thereby determining the transfer device work probability. It memorize | stores in the memory | storage part 210 (refer FIG. 7).
Note that step S1902 may be performed after steps S1903 and S1904.

Next, in step S1905, the constraint equation setting unit 215 sets the constraint condition setting values stored in the constraint condition storage unit 214 to equations (1), (2), and (4) to (9). based value set _{(shipLotSize ig, MixCondhition ig, i} , M, MixCondition ig, i, MaxloadAmount j, MaxDayShip, craneMax m, j, craneRatio i, m, loadOrderAmount i, j) a.

Next, in step S1906, the evaluation function setting unit 218 uses the expression (10) to store the “weighting factor w ₁ for the shipping deadline violation amount and the weighting factor w ₂ for the empty load amount stored in the planning policy storage unit 217. , A weighting factor w ₃ ”for the number of phase products is set. In addition, the evaluation function setting unit 218 calculates the loadAmountRef _{i, j in the} expression (11) based on the target shipment amount value loadOrderAmount _{i, r} obtained from the shipping resource matrix 800 stored in the shipping resource matrix storage unit 212. Set. In addition, the evaluation function setting unit 218 sets the load availability condition MixCondition _{ig, i} stored in the constraint condition storage unit 214 as a variable that defines loadMixNig _{, j in the} equation (14).
Note that step S1905 may be performed after step S1906.

Next, in step S1907, the optimization unit 219 performs optimization calculation by the mixed integer programming method, and the constraints based on the constraint equations ((1) to 9 (equation)) set by the constraint equation setting unit 215. A determination variable (such as shipment quantity loadAmount _{i, j} by shipment group i, shipment date j _, etc.) that minimizes the evaluation function (Equation (10)) set by the evaluation function setting unit 218 within a range that satisfies the above is obtained. Next, in step S1908, the shipment plan output unit 220, based on the optimization calculation result by the optimization unit 219, the shipment amount loadAmount _{i, j} for each shipment group i and each shipment date j, ship date j different phases product number LoadMixN _ig, and _j, shipping date j by ship ig different payload _{(shipLotSize ig, j -LoadRatioCover ig,} j) and, shipping group i, the shipment date j of transport equipment A work amount (loadAmount _{i, j} × craneRatio _{i, m} ), an OK button, and an NG button are displayed on the display device 106 (see FIGS. 15 to 18).

Next, in step S1909, the shipment plan output unit 220 determines which of the OK button and the NG button is pressed by the operator. If the operator presses the OK button as a result of this determination, it is determined that the shipping plan has been accepted by the operator, and the processing according to the flowchart of FIG. 19 ends.
On the other hand, if the operator presses the NG button, it is determined that the shipping plan has not been accepted by the operator, and the process returns to step S1905. Then, the operator changes at least one of the constraint condition setting value and the planning condition setting value to update the storage contents of the constraint condition storage unit 214 and the planning policy storage unit 217. Thereafter, the processing after step S1905 is executed with the updated contents.

As described above, in the present embodiment, as described, the ship due date violations amount evaluation value J _1, an empty payload evaluation value J _2, constraint on the evaluation function J to the sum-out weighted phase product number evaluation value J ₃ minimizes As a condition, a product obtained by multiplying a shipment amount loadAmount _{i, j} (shipment plan) of a product belonging to the shipment group i by a transport device work occurrence probability craneRatio _{i, m} of the transport device m in the shipment group i is the operator. The constraint condition is set so as to be less than the upper limit value craneMax _{m, j of} the transport device set by. Therefore, in addition to complying with the product shipment deadline date and minimizing the empty loading capacity of the product transportation means (for example, ship), the work volume of each transporting device that performs shipping work should be leveled as much as possible. It becomes possible to devise a shipping plan that takes into account
Further, in the present embodiment, by introducing the phase product evaluation value J ₃ into the evaluation function, it becomes possible for one ship to load orders (products) belonging to a plurality of shipping groups (that is, a phase). Product can be allowed). Further, the weighting factor w ₂ for an empty payload evaluation value J _2, a weighting factor w ₃ for phase product number evaluation value J _3, by an operator to adjust with reference to the delivery schedule, the relation of mutual trade-off A certain balance between “empty load and multiple ports” can be achieved.

<Modification>
As shown in the comparison result between Case 2 and Case 3 in FIG. 14, when the “cross product” is made possible, the empty load amount can be reduced, and therefore, a shipping plan for reducing the empty load amount is made. However, it is preferable to enable “phase product” as in the case 3, but it is not always necessary to enable “phase product”. In addition, in order to make the “compound product” impossible in this way, in addition to setting the constraint equation (15), the evaluation function and constraint equation do not consider the information related to the phase product evaluation value J _3. It can also be done.

  Further, the shipment group definition logic is not limited to that shown in FIG. For example, as described in the present embodiment, first, orders (products) having the same “transportation mode” and “destination” are set as one shipping group. After that, for all orders (products), a shipping group having the same “transportation form” and the same country of “exporting country” is extracted, and among the extracted shipment groups, “transportation form” ”And“ exporting country ”may be grouped together and regrouped together. In addition, for shipping groups that include orders (products) with different contents of “customer witness”, the shipping groups are divided and regrouped so that orders with the same “customer witness” content become one shipping group. You may make it make it. Further, the shipping group may be determined using “shipping date” as a key.

In the present embodiment, the transport device work occurrence probability 700 for each shipping group is calculated based on the transport result data 600 of each transport device for each shipping group. May be input by an operator.
Further, processing may be performed for each product without performing processing for each shipment group.

[Second Embodiment]
Next, a second embodiment of the present invention will be described. In the first embodiment described above, as described with reference to FIG. 13, in order to minimize the shipping deadline day violating amount evaluation value J ₁ , all of the shipping volume preceding the shipping deadline date r (region 1301). As an example, the case where all of the difference between the shipping amount after the shipping deadline date r and the target shipping amount (area 1302) is minimized is described. On the other hand, in the present embodiment, a case where the difference from the target shipment amount is not considered for a predetermined period including the shipment deadline date will be described as an example. Thus the present embodiment and the first embodiment described above, part of the configuration and processing to minimize shipping deadline violations amount evaluation value J ₁ is mainly different. Therefore, in the description of the present embodiment, the same parts as those in the first embodiment described above are denoted by the same reference numerals as those in FIGS.

FIG. 20 is a diagram illustrating an example of the relationship between the target shipment amount loadAmountRef _{i, j} and the shipment amount loadDeadlineAmount _{i, r, j ′} . These are the same as those shown in FIG. 13 (see equation (12)). As described above, in the first embodiment, the areas 1301 and 1302 shown in FIG. 13 are minimized, but in the present embodiment, the areas 2001 and 2002 are minimized. That is, in the period in which the difference between the target shipping amount loadAmountRef _{i, j} and the shipping amount loadDeadlineAmount _{i, r, j ′} occurs, from X days before the shipping deadline date r to Y days after the shipping deadline date r. For the dead zone period (the dead zone number is a), these differences are not taken into consideration. X and Y may be the same number or different numbers. Further, either one of X and Y may be “0”.

Therefore, the evaluation function setting unit 218 sets the shipping date violation amount evaluation value J ₁ as in the following equation (16) instead of the equation (11).

It is assumed that the information specifying the dead zone number a shown in the equation (16) is acquired by the planning policy acquisition unit 216 and stored in the planning policy storage unit 217.
The optimization unit 219 performs an optimization calculation using the shipping date violation amount evaluation value J ₁ set by the equation (16).
FIG. 21 is a diagram illustrating an example of a result of optimization calculation by the optimization unit 219. In FIG. 21, Case 3 (first example) is the same as Case 3 shown in FIG. 14, and is an example of a result when no dead zone period is provided. On the other hand, Case 4 (second example) is the one of the present embodiment, and is an example of the result when the dead zone period is not provided. Case 3 and case 4 differ only in the presence or absence of a dead zone period. Case 4 is the result when the dead zone period X = Y = 1 day.

  In FIG. 21, when comparing the case 3 and the case 4, by providing a dead zone period, in the vicinity of the shipping deadline date, the restriction on the shipping deadline date of the order (product) is eliminated, thereby delaying the shipping deadline date. Although the weight of the product shipped (delayed due date) increases from 3499 [ton] to 3924 [ton], the delayed due date outside the dead zone is 616 [ton], and many delays occur. In addition, it can be seen that the number of multi-ports can be reduced from “18” to “10” (see the delay in the shipping deadline in FIG. 21, the number of multi-ports). That is, by setting the dead zone period, it is possible to easily devise a shipping plan that prioritizes the number of multi-ports rather than the shipping deadline date.

[Third Embodiment]
Next, a third embodiment of the present invention will be described. In the first and second embodiments described above, the constraint equation (9) is set on the premise that each transfer device processes all of the product on the product shipment date. On the other hand, in this embodiment, the case where each conveyance apparatus can process the said product besides the shipping date of a product is assumed. In the description of this embodiment and the fourth embodiment to be described later, the shipping operation by the transfer device is referred to as “processing”. That is, in the present embodiment, a case will be described in which the constraint equation of Equation (9) is changed assuming a case where a certain product is processed by the transport device n days before the shipment date (n is a positive integer). As described above, the present embodiment is mainly different from the first and second embodiments described above in the processing for setting a constraint equation in place of the constraint equation (9). Therefore, in the description of the present embodiment, the same parts as those in the first and second embodiments described above are denoted by the same reference numerals as those in FIGS. . In the present embodiment, for the sake of simplicity, only two cranes, the crane 1 and the crane 2, are considered in the shipping plan creation, and a case where the crane carries a steel plate as a product will be described as an example.

Also in the present embodiment, as in the first embodiment, the order information acquisition unit 201 indicates the attribute of the order (product) based on the operation input of the PD 104 by the operator or the communication with the external device via the network. Order information is acquired and stored in the order information storage unit 202. However, in the present embodiment, the order information acquisition unit 201 acquires order information further including the shipping date and the processing date of the transport device as attributes in addition to the attributes illustrated in FIG.
FIG. 24 is a diagram illustrating an example of the order information 2400. In the example shown in FIG. 24, with respect to the “order information of orders already manufactured / shipped” shown in FIG. Crane 2 processing date), shipping date based on the processing date of crane 1 (crane 1 processing date-shipping date), shipping date based on the processing date of crane 2 (crane 2 processing date-shipping date), Is added. In the following description, “processing date−shipping date” is referred to as “processing date reference shipping date” as necessary.

Moreover, in this embodiment, the conveyance apparatus work probability calculation part 209 performs the following processes.
First, as described in the first embodiment, the transfer device work probability calculation unit 209 calculates a transfer device work occurrence probability 700 for each shipment group and stores it in the transfer device work probability storage unit 210 (see FIG. 7). reference). As described above, the transfer device work occurrence probability is the number of use of each transfer device in each shipment group by the number of “manufactured / shipped orders (products)” belonging to the shipment group (the number of products in FIG. 6). Divided value.
In this embodiment, the transfer device work probability calculation unit 209 totals the processing amounts of the cranes 1 and 2 on all the shipping dates based on the processing date based on the order information 2400, and sets the processing date. The processing amount of each transport device by reference shipment date is calculated and stored in the transport device work probability storage unit 210.
FIG. 25 is a diagram illustrating an example of the processing amount 2500 of each transport device by processing date reference shipping date. 24, the specific contents are not shown, but the order information 2400 whose product number is “11” or later is also aggregated (aggregation for all products included in the order information 2400). "Processing amount 2500 of each transport device by processing date reference shipping date" is obtained.

  In FIG. 25, “processing date−shipping date” of “0” indicates that the crane processed the product on the shipping date, and “processing date−shipping date” is “−n (n is a positive integer)”. "Indicates that the crane has processed the product n days before the shipment date (the product is shipped n days after the date the crane processed the product (processing date)). In addition, in FIG. 25, the case where the processing amount of a conveyance apparatus (crane) is a weight is mentioned as an example. However, the throughput of the transport device (crane) is not limited to weight. For example, the processing amount of the transport device (crane) may be the number of sheets. When the processing amount of the transport device (crane) is set to the number of sheets, the number of sheets can be converted into the weight by using the average weight per one steel plate as a product. Similarly, the weight can be converted into the number of sheets.

  In the example shown in FIG. 25, the crane 1 is gradually working from 3 days before the shipping date (“processing date−shipping date” is “−3”), whereas the crane 2 is shipping the shipping date. Most of the work is performed on the same day (the day when “processing date−shipping date” is “0”). Thus, depending on the transport device, the work may not be concentrated only on the day of shipment. Therefore, in the present embodiment, as shown below, instead of the equation (9), a constraint equation is set so that the processing amount that can be processed by the transfer device on a certain processing date is equal to or less than the transfer device capacity upper limit value.

  First, the transfer device work probability calculation unit 209 calculates the transfer device work probability of each transfer device for each processing date based on the processing date based on the processing amount 2500 of each transfer device for each process date based on the shipment date. Store in the storage unit 210. In the present embodiment, a description will be given of a case where all preset processing date reference shipment dates (in this embodiment, “0”, “−1”, and “−2” are set in advance. Details are described in the following paragraph [0109]. ], The total processing amount of the transporting device in (1)) is divided by the processing amount of the transporting device on the individual processing date standard shipment date ("0", "-1" or "-2"). By performing for each device, the transfer device processing probability of each transfer device for each processing date based on the shipment date can be obtained. In other words, the transport device processing probability is based on the individual processing date standard shipment for the total processing amount of the transport device on all the dates set as the processing date standard shipping date, which is the shipping date based on the processing date of the transporting device. It is the ratio of the processing amount of the transfer device in a day.

  Here, from the result of “the processing amount 2500 of each transport device for each processing date based shipping date” shown in FIG. 25, the processing date based shipping date (“processing date−shipping date”) out of the total processing amount of the transporting device. It can be seen that the processing amount of the transport device between “−2” and “0” is dominant. Therefore, in the present embodiment, “−2”, “−1”, and “0” are set in advance as the processing date reference shipping date (“processing date−shipping date”) used for the calculation. Then, based on the processing amount of the transfer device between these process date reference shipment dates (“process date−shipment date”) “−2” to “0”, the transfer of each transfer device by the process date reference shipment date Obtain the equipment processing probability.

FIG. 26 is a diagram illustrating an example of the transfer device processing probability 2600 of each transfer device for each processing date reference shipment date.
In FIG. 25, for example, the total processing amount of the crane 1 when the processing date reference shipping date (“processing date−shipping date”) is “−2”, “−1”, and “0” is 9890 (= 994 + 1974 + 6922). [Ton]. Among them, the processing amount of the crane 1 on the day when the processing date reference shipping date is “0” is 6922 [ton]. Therefore, the conveyance device processing probability of the crane 1 when the processing date reference shipping date is “0” is 0.70 (= 6922/9890).
As described above, in this embodiment, “−2”, “−1”, and “0” are set in advance as the processing date base shipping date (“processing date−shipping date”) used for the calculation. Then, the value of the transport device processing probability when the processing date base shipping date (“processing date−shipping date”) is “−3” to “−5” is “0.00”. However, the processing date base shipping date (“processing date−shipping date”) to be set is not limited to “−2”, “−1”, and “0”. For example, a range longer than this may be set as the range of the processing date reference shipping date used for the calculation. If the range of the processing date base shipping date to be set is long, the calculation accuracy increases but the calculation load increases. In consideration of the fact that the calculation accuracy and the calculation load are in a trade-off relationship as described above, it is possible to determine the processing date-based shipping date to be set according to the plan target.
In the present embodiment, for example, an example of the transport device processing probability acquisition unit is realized by the processing for calculating the transport device processing probability 2600 by the transport device work probability calculation unit 209 as described above.

The constraint storage unit 214 stores the upper limit value of the work amount of the transfer device m on the processing date s as the transfer device capability upper limit value (craneMax _{m, s} ) instead of the transfer device capability upper limit value (craneMax _{m, j} ). . Here, as in the first embodiment, the work amount is represented by the weight [ton] of the product transported by the crane. Here, the case where the transport device capacity upper limit (craneMax _{m, s} ) is the same as that shown in FIG. 9 will be described as an example. That is, the case where the transport device capacity upper limit (craneMax _{m, s} ) does not change from day to day will be described as an example.

  The constraint equation setting unit 215 sets a constraint equation of the following linear inequality equation (17) instead of the equation (9).

The loadAmount _{i, sv on} the left side of the equation (17) is the shipping group on the date (shipping date _sv ) obtained by subtracting the processing date base shipping date (processing date-shipping date) v from the processing date s of the transport device m. This is a decision variable (a real number greater than or equal to 0) representing the shipment amount of the product belonging to i. Here, the shipping date s-v is the same as the shipping date j shown in the equation (9). Also, craneRatio _{i, m} is a transfer device work occurrence probability 700 of the transfer device m in the shipping group i, and is stored in the transfer device work probability storage unit 210. Further, CraneDistribution _{m, v} is the transport device processing probability 2600 of the transport device m on the processing date reference ship date (process date−ship date), and is stored in the transport device work probability storage unit 210. On the other hand, craneMax _{m, s on} the right side of the equation (17) is the upper limit value of the transport device capacity of the transport device m on the processing date s, and is stored in the constraint condition storage unit 214.
As described above, the expression (17) indicates that the amount (weight) that the transport device m transports all the products belonging to the shipping group i on the processing date s must be less than or equal to the transport device capacity upper limit value. ing.

The optimization unit 219 uses the constraint equation (17) instead of the equation (9) to determine a decision variable (shipment group) that minimizes the evaluation function (equation (10)) set by the evaluation function setting unit 218. (i.e., shipment amount loadAmount _{i, j,} etc.) from the warehouse for each i and each shipping date j). Since the determination variable is obtained in the same manner as in the first and second embodiments, detailed description thereof is omitted here.
FIG. 27 is a diagram illustrating an example of a result of optimization calculation by the optimization unit 219. “Transport equipment restriction”, “Possibility of mutual product”, “Number of dead zones”, “Transport equipment restriction violation amount”, “Empty load amount”, “Delayed shipping deadline”, and “Number of multi-ports” shown in FIG. This is the same as that shown in FIGS. “Crane processing distribution” is “present” indicates that the constraint equation (17) is used instead of equation (9).

The shipment plan output unit 220 displays the shipment amount loadAmount _{i, j} for each shipment group i and each shipment date _j on the display device 106 based on the result of the optimization calculation by the optimization unit 219. In addition to this, in the present embodiment, the shipment plan output unit 220 includes the number of phase loads loadMixN _{ig, j for each} ship ig and each shipment date j, the load amount (loadAmountG _{ig, j} ) for each shipment date j and each ship ig, Information related to the work amount (loadAmount _{i, s} × craneRatio _{i, m} ) of the transport device for each shipment group i and each processing date s is displayed on the display device 106. Here, the case where case 5 of FIG. 27 is implemented will be described as an example.

FIG. 28 is a diagram illustrating an example of the shipment amount (shipment plan) from the warehouse for each shipment group i and each shipment date j.
In FIG. 28, for example, it is shown that the order (product) belonging to the shipping group A is planned to be shipped 846 [ton] on the day of the shipping date “1” (the day after the shipping planning date). .
FIG. 29 is a diagram illustrating an example of the number of phase products for each ship ig and each shipping date j.
In FIG. 29, for example, on the day when the shipping date is “1” (the day after the shipping planning date), the ship 1 does not perform mutual product (number of mutual products = “1”), and the ship 2 has two This indicates that the orders (products) belonging to the shipping group are crossed (number of cross products = “2”).

FIG. 30 is a diagram illustrating an example of the loading amount by ship date j and ship ig.
In FIG. 30, for example, on the day when the shipping date is “1” (the day after the shipping planning date), an order (product) of 846 [ton] is loaded on the ship 1, and 1836 [ton] is loaded on the ship 2. Indicates that the order (product) is to be loaded.
FIG. 31 is a diagram illustrating an example of the work amount of the transport device by shipment group i and by processing date s. Specifically, FIG. 31A shows an example of the work amount of the crane 1, and FIG. 31B shows an example of the work amount of the crane 2.
In FIG. 31 (a), for example, on the day when the processing date is “1” (the day after the shipping planning date), the crane 1 transports orders (products) belonging to the shipping group A 586 [ton] Orders (products) belonging to the shipping group B are transported 48 [ton], orders (products) belonging to the shipping group C are transported 566 [ton], and orders (products) belonging to the shipping group D are transported 187 [ton]. , A total of 1388 [ton] orders (products) are conveyed.

In FIG. 31B, for example, on the day when the processing date is “1” (the day after the shipping planning date), the crane 2 transports the orders (products) belonging to the shipping group A by 160 [ton]. Then, the order (product) belonging to the shipping group B is transported by 67 [ton], the order (product) belonging to the shipping group C is transported by 158 [ton], and the order (product) belonging to the shipping group D is transported by 786 [ton]. It indicates that the order (product) of 1171 [ton] is transported in total.
As shown in FIG. 31, on any shipping date, the total value of the work amounts of the cranes 1 and 2 is suppressed to the transport device capacity upper limit 900 (= 1700 [ton]) or less shown in FIG. I understand that

As described above, in the present embodiment, the transfer device m of the transfer device m on all the process date reference shipment dates v (“−2” to “0”) set as the shipment date based on the process date s of the transfer device m. A transport device processing probability CraneDistribution _{m, v} , which is a ratio of the processing amount of the transport device m on the individual processing date reference shipment date to the total processing amount, is calculated. Then, the shipment amount loadAmount _{i, sv} of the product according to the shipping date s−v (= j) _, the transfer device work occurrence probability craneRatio _{i, m} of the transfer device m, and the transfer device processing probability CraneDistribution _m, of the transfer device _{m The} value obtained by multiplying _v by the sum of all the set processing date reference shipping dates “−2” to “0” is the transport device capacity upper limit value craneMax _m, Set a constraint expression indicating that it is less than or equal to _s . As described above, in this embodiment, the amount (weight) that the transport device m transports products belonging to all the shipping groups i on the processing date s must be equal to or less than the transport device capacity upper limit craneMax _{m, s.} Develop a shipping plan under. Therefore, by predicting the date on which the product is processed by the transport device m from the past results, the work of the transport device m can be leveled based on the processing date, and the shipment closer to the actual state of the work mode of the transport device I can make a plan.
In the above description, “processing date−shipping date” may be “shipping date−processing date”. For example, in the equation (17), when “shipment date−processing date” is adopted, loadAmount _{i, sv} becomes loadAmount _{i, s + v} .
Also in this embodiment, the modified example shown in the first embodiment can be adopted.

[Fourth Embodiment]
Next, a fourth embodiment of the present invention will be described. In the above-described first to third embodiments, the case has been described in which the scheduled shipping date is not fixed for all products included in the order information of the order to be shipped. However, there are cases where the shipping date is fixed for some of the products included in the order information. Therefore, in the present embodiment, a value obtained by subtracting the amount necessary for processing a product for which the planned shipping date is fixed is shown in Equation (17) so that a shipping plan can be made for such a case. A case where the transport device capacity upper limit value craneMax _{m, s} is set will be described. As described above, the present embodiment is mainly different from the third embodiment described above in the processing for setting the transport device capacity upper limit value craneMax _{m, s} of Expression (17). Therefore, in the description of the present embodiment, the same parts as those in the first to third embodiments described above are denoted by the same reference numerals as those in FIGS. In the present embodiment, for the sake of simplicity, only two cranes, the crane 1 and the crane 2, are considered in the shipping plan creation, and a case where the crane carries a steel plate as a product will be described as an example.

  FIG. 32 is a diagram illustrating an example of a functional configuration of the shipping plan planning apparatus 3200. The shipping plan planning apparatus 3200 functions as a function of the shipping plan planning apparatus 100 shown in FIG. 2 as a confirmed order information acquisition unit 3201, a confirmed order information storage unit 3202, a shipping group determination unit 3203, and a shipping group. A storage unit 3204, a confirmed order matrix generation unit 3205, a confirmed order matrix storage unit 3206, and a confirmed order transportation equipment work amount calculation unit 3207 are included.

In the first embodiment, the order information acquisition unit 201 acquires all order information scheduled to be shipped and stores it in the order information storage unit 202. On the other hand, in the present embodiment, the order information acquisition unit 201 acquires order information of a product (unconfirmed order) for which the planned shipping date is not confirmed from the order information scheduled to be shipped, and the order information storage unit 202 To remember.
On the other hand, the confirmed order information acquisition unit 3201 acquires the order information of the product (confirmed order) for which the scheduled shipping date is confirmed among the order information, and stores it in the confirmed order information storage unit 3202.
In the following description, “order information for products whose shipping date is not fixed” will be referred to as “unfixed order information” as necessary, and “order information for products whose shipping date will be fixed”. This is referred to as “confirmed order information” as necessary.

FIG. 33 is a diagram showing an example of confirmed order information 3300. The unconfirmed order information is, for example, order information 301 shown in FIG.
As shown in FIG. 33, the firm order information 3300 includes information on the expected shipping date in addition to the product number, size, weight, shipping deadline date, transportation mode, destination, exporting country, and customer presence. Yes. The scheduled shipping date is the date on which the product is scheduled to be shipped (shipped) from the warehouse. Here, the date of planning the shipping plan is represented by “0”, and the date Z (Z is a natural number) after the planning date of the shipping plan is represented by “Z”.
The confirmed order information acquisition unit 3201 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108). Further, the confirmed order information storage unit 3202 is realized by using, for example, the HD 105.
In this embodiment, for example, an example of a shipping date information acquisition unit is realized by the processing of the confirmed order information acquisition unit 3201 as described above.

The shipping group determination unit 3203 sends each order (product) related to the confirmed order information 3300 stored in the confirmed order information storage unit 3202 in accordance with the shipping group definition logic stored in the shipping group definition logic storage unit 206. And stored in the shipment group storage unit 3204.
Also in the present embodiment, as in the first embodiment, the shipping group determination unit 3203 refers to the confirmed order information 3300 and selects one order (product) having the same “transportation mode” and “destination”. Are grouped into one of the shipping groups A to D, and the classification result is stored in the shipping group storage unit 3204.
The shipping group determination unit 3203 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108). Further, the shipment group storage unit 3204 is realized by using, for example, the HD 105.
In the present embodiment, for example, an example of the second shipment group generation unit is realized by the processing of the shipment group determination unit 3203 as described above.

The confirmed order matrix generation unit 3205 is based on the confirmed order information 3300 (see FIG. 33) stored in the confirmed order information storage unit 3202 and the shipping group classification result stored in the shipping group storage unit 3204. The firm order matrix is generated and stored in the firm order matrix storage unit 3206.
FIG. 34 is a diagram showing an example of the confirmed order matrix 3400. As shown in FIG.
In FIG. 34, a confirmed order matrix 3400 represents a shipment amount (order amount) for each shipment group and each scheduled shipment date. In the example shown in FIG. 34, the shipment amount is represented by the product weight [ton]. Here, if the “scheduled shipping date” is the day of the planning date of the shipping plan, the “scheduled shipping date” is set to “0”, and the “scheduled shipping date” is Z (Z is the planning date of the shipping plan). If it is a natural number) days later, the “scheduled shipping date” is “Z”. In the example shown in FIG. 34, it is shown that the products belonging to the shipping group A are scheduled to be shipped after 2 days and 4 days after the planning date of the shipping plan. In FIG. 34, the case where the shipment amount is stored only for the shipment group A is described as an example. However, according to the classification result of the shipment group, the confirmed order matrix 3400 includes the shipment groups A to D. The shipment amount is stored for at least one of the above.
The confirmed order matrix generation unit 3205 is realized by using, for example, the CPU 101, the ROM 102, and the RAM 103. Further, the confirmed order matrix storage unit 3206 is realized by using the HD 105, for example.
In the present embodiment, an example of a confirmed order matrix generation unit is realized by the processing of the confirmed order matrix generation unit 3205 as described above.

The confirmed order transportation device work amount calculation unit 3207 includes a “confirmed order matrix 3400” stored in the confirmed order matrix storage unit 3206 and a “conveyance device work generation by shipment group” stored in the transport device work probability storage unit 210. Based on “probability 700”, the amount of generated load on the transport device for each scheduled shipping date is obtained.
FIG. 35 is a diagram illustrating an example of the transport device load generation amount 3500 of each transport device by scheduled shipping date.
In FIG. 35, a transport device load generation amount 3500 of each transport device by scheduled shipping date represents a processing amount (transport amount) for the confirmed order information of each transport device by scheduled shipping date. In the example shown in FIG. 35, the processing amount is represented by the product weight [ton]. Here, if the “scheduled shipping date” is the day of the planning date of the shipping plan, the “scheduled shipping date” is set to “0”, and the “scheduled shipping date” is Z (Z is the planning date of the shipping plan). If it is a natural number) days later, the “scheduled shipping date” is “Z”.
For example, in the confirmed order matrix 3400 shown in FIG. 34, the order quantity of the shipping group A when the planned shipping date is “2” is 1500 [ton]. Further, in the transport equipment work occurrence probability 700 for each shipping group shown in FIG. 7, the “transport equipment work occurrence probability of the shipping group A” in the crane 1 is “0.8”. Accordingly, the confirmed order transportation equipment work amount calculation unit 3207 sets the “shipping equipment work occurrence probability of the shipping group A in the crane 1” to “1500” which is the order quantity of the shipping group A when the scheduled shipping date is “2”. Is multiplied by “0.8”, and “1200” is obtained as the processing amount to be transported by the crane 1 on the scheduled shipping date “2” for the product for which the scheduled shipping date is determined.
The values in other columns in FIG. 35 are also obtained by performing the same calculation as the above calculation.
In the present embodiment, for example, an example of a load generation amount calculation unit for each scheduled shipping date is realized by the calculation processing of the transfer device load generation amount 3500 by the transfer device work amount calculation unit 3207 for the confirmed order as described above.

The confirmed order transportation device work amount calculation unit 3207 includes “a transportation device load generation amount 3500 of each transportation device by scheduled shipping date” shown in FIG. 35 and “each transportation device by processing date reference shipment date” shown in FIG. "Transport equipment load generation amount for each processing day" is obtained based on "the transport equipment processing probability 2600".
FIG. 36 is a diagram illustrating an example of the transport device load generation amount 3600 for each processing day.
In FIG. 36, a transport device load generation amount 3600 by processing date represents a processing amount (conveyance amount) for the confirmed order information of each transport device by processing date. In the example shown in FIG. 36, the processing amount is represented by the product weight [ton]. Also, here, when the “processing date” is the day of the planning date of the shipping plan, the “processing date” is set to “0”, and the “processing date” is Z (Z is a natural number) days after the planning date of the shipping plan. In this case, the “processing date” is “Z”.

  For example, in the “conveyance device load generation amount 3500 by scheduled shipping date” shown in FIG. 35, the processing amount of the crane 1 when the scheduled shipping date is “2” is 1200 [ton]. In addition, in the “transport device processing probability 2600 of each transport device by processing date standard shipping date” shown in FIG. 26, the crane 1 is transported when the processing date standard shipping date (processing date−shipping date) is “−2”. The device processing probability is “0.1”. Therefore, the confirmed-order-conveyance-conveying-device work amount calculation unit 3207 sets the processing date reference shipping date (processing date−shipping date) to “1200”, which is the processing amount of the crane 1 when the shipping date is “2”. -2 "is multiplied by" 0.1 ", which is the transfer device processing probability of the crane 1, and the product for which the planned shipping date is fixed is defined as the processing amount that the crane 1 transfers to the processing date" 0 ". Obtain “120”.

Further, for example, in the “conveyance device load generation amount 3500 by scheduled shipping date” shown in FIG. 35, the processing amount of the crane 1 when the scheduled shipping date is “2” is 1200 [ton]. The processing amount of the crane 1 when “4” is “4” is also 1200 [ton]. In addition, in the “transport device processing probability 2600 of each transport device by processing date standard shipping date” shown in FIG. 26, the crane 1 is transported when the processing date standard shipping date (processing date−shipping date) is “−2”. The equipment processing probability is “0.1”, and the handling equipment processing probability of the crane 1 when the processing date base shipping date (processing date−shipping date) is “0” is “0.7”. Therefore, the confirmed-order-conveyance-conveying-device work amount calculation unit 3207 sets the processing date reference shipping date (processing date−shipping date) to “1200”, which is the processing amount of the crane 1 when the scheduled shipping date is “2”. The value obtained by multiplying “0.7”, which is the transfer device processing probability of the crane 1 when “0”, and “1200”, which is the processing amount of the crane 1 when the scheduled shipping date is “4”, A product whose shipping date is fixed by adding a value multiplied by “0.1”, which is the conveyance device processing probability of the crane 1 when the shipping date (processing date−shipping date) is “−2”. On the processing date “2”, “960 (= 120 × 0.7 + 120 × 0.1)” is obtained as the processing amount transported by the crane 1.
The values in the other columns in FIG. 36 are also obtained by performing the same calculation as the above calculation.
In the present embodiment, for example, an example of the daily processing load generation amount calculation unit is realized by the calculation processing of the transport device load generation amount 3600 by the transport device work amount calculation unit 3207 for the confirmed order as described above.

Based on the “conveyance device load generation amount 3600 for each processing day” shown in FIG. 36 and the “conveyance device capacity upper limit 900” shown in FIG. Calculate the transfer device capacity net upper limit of each transfer device.
FIG. 37 is a diagram illustrating an example of the transfer device capacity net upper limit 3700 of each transfer device for each processing date.
In FIG. 37, the transport device capacity net upper limit value 3700 for each processing day is a value obtained by subtracting the transport device load generation amount 3600 of each transport device for each processing day from the transport device capacity upper limit value 900. In FIG. 37, the upper limit value of the processing amount of the transport device is represented by [ton]. Also, here, when the processing date is the day of planning the shipping plan, the processing date is “0”, and when the processing date is Z (Z is a natural number) days after the planning date of the shipping plan, The processing date is “Z”.

For example, in the “transport equipment capacity upper limit 900” shown in FIG. 7, the transport equipment capacity upper limit of the crane 1 is 1700 [ton]. In addition, in the “conveyance device load generation amount 3600 by processing day” shown in FIG. 36, the processing amount for the confirmed order information of the crane 1 when the processing date is “0” is 120 [ton]. Therefore, the confirmed order transportation device work amount calculation unit 3207 is the processing amount corresponding to the confirmed order information of the crane 1 when the processing date is “0” from “1700” which is the transport device capacity upper limit value of the crane 1. By subtracting “120”, “1580” is obtained as the upper limit value of the processing amount of the crane 1 on the processing date “0” (conveyance equipment capacity net upper limit value) in consideration of the confirmed order information.
The values in other columns in FIG. 37 are also obtained by performing the same calculation as the above calculation.

The constraint condition storage unit 214 rewrites the transport device capacity upper limit value (craneMax _{m, s} ) shown in FIG. 9 to the transport device capacity net upper limit value 3700 of each transport device obtained as described above.
Then, the constraint equation setting unit 215 sets the value of the transport device capacity net upper limit value 3700 of each transport device for each processing date in the craneMax _{m, s} on the right side in the constraint equation of the linear inequality equation (17). In the first to third embodiments, the value of the transport device capacity upper limit value (craneMax _{m, j} , craneMax _{m, s} ) does not change depending on the day, but in this embodiment, the transport device capacity upper limit value depends on the processing date s. The value of (craneMax _{m, s} ) will change.
In the present embodiment, for example, an example of the upper limit value calculation unit is realized by the calculation processing of the transfer device capacity net upper limit value 3700 by the transfer device work amount calculation unit 3207 for the confirmed order as described above.

The optimization unit 219 uses the constraint equation (17) to determine a decision variable (by shipment group i, by shipment date j) that minimizes the evaluation function (equation (10)) set by the evaluation function setting unit 218. The amount of shipment from the warehouse of loadAmount _{i, j,} etc.) is obtained. Since the determination variable is obtained in the same manner as in the first to third embodiments, detailed description thereof is omitted here.
FIG. 38 is a diagram illustrating an example of a result of optimization calculation by the optimization unit 219. “Transport equipment constraint”, “Possibility of mutual product”, “Dead zone days”, “Transport equipment constraint violation amount”, “Empty load amount”, “Delayed shipping deadline”, “Number of multi-ports” shown in FIG. This is the same as that shown in FIGS. The “crane processing distribution” is the same as that shown in FIG.

The shipment plan output unit 220 displays the shipment amount loadAmount _{i, j} for each shipment group i and each shipment date _j on the display device 106 based on the result of the optimization calculation by the optimization unit 219. In addition to this, in the present embodiment, the shipment plan output unit 220 includes the number of phase loads loadMixN _{ig, j for each} ship ig and each shipment date j, the load amount (loadAmountG _{ig, j} ) for each shipment date j and each ship ig, Information related to the work amount (loadAmount _{i, s} × craneRatio _{i, m} ) of the transport device for each shipment group i and each processing date s is displayed on the display device 106. Here, a case where case 6 of FIG. 38 is implemented will be described as an example.

FIG. 39 is a diagram illustrating an example of the shipment amount (shipment plan) from the warehouse for each shipment group i and each shipment date j.
In FIG. 39, for example, it is shown that the order (product) belonging to the shipping group B is planned to be shipped 613 [ton] on the day with the shipping date “1” (the day after the shipping planning date). .
FIG. 40 is a diagram illustrating an example of the number of phase products for each ship ig and each shipping date j.
In FIG. 40, for example, on the day when the shipping date is “1” (the day after the shipping planning date), nothing is loaded on the ship 1 (number of phase products = “0”), and two on the ship 2 This indicates that the orders (products) belonging to the shipping group are crossed (number of cross products = “2”).

FIG. 41 is a diagram illustrating an example of the loading amount by ship date j and ship ig.
In FIG. 41, for example, on the day when the shipping date is “1” (the day after the shipping planning date), nothing is loaded on the ship 1 (0 [ton]), and the ship 2 has 2000 [ton]. Indicates that an order (product) is to be loaded.
FIG. 42 is a diagram illustrating an example of the work amount of the transport device for each shipment group i and for each processing date s. Specifically, FIG. 42A shows an example of the work amount of the crane 1, and FIG. 42B shows an example of the work amount of the crane 2.
In FIG. 42 (a), for example, on the day when the processing date is “1” (the day after the shipping planning date), the crane 1 transports orders (products) belonging to the shipping group A 154 [ton] An order (product) belonging to the shipping group B is conveyed by 86 [ton], an order (product) belonging to the shipping group C is conveyed by 777 [ton], and an order (product) belonging to the shipping group D is conveyed by 75 [ton]. , A total of 1091 [ton] orders (products) are conveyed.

In FIG. 42B, for example, the crane 2 does not carry orders (products) belonging to the shipping group A on the day of the processing date “1” (the day after the shipping planning date) (0). [Ton]), orders (products) belonging to the shipping group B are conveyed 436 [ton], orders (products) belonging to the shipping group C are conveyed 247 [ton], and orders (products) belonging to the shipping group D are 106. [Ton] is transported, and a total of 789 [ton] orders (products) is transported.
As shown in FIG. 42, the total value of the work amount of each crane 1 and 2 on any shipping date is suppressed to the transport device capacity net upper limit 3700 or less for each processing day shown in FIG. (For example, the planned processing amount when the processing date of the crane 1 is “0” is “758”, whereas the net upper limit value of the transfer device capacity when the processing date of the crane 1 is “0” is 1580. [Ton]).

  As described above, in the present embodiment, the confirmed order matrix 3400 representing the shipment amount of the product for each shipment group i and each scheduled shipment date is generated based on the confirmed order information 3300 for which the scheduled shipment date is confirmed. Next, the shipping amount of the product shown in the firm order matrix 3400 is multiplied by the transport device work occurrence probability 700 of each transport device m in the same shipping group i as the shipping group i to which the product belongs. The amount of generated load 3500 for each transfer device for each day is obtained. Next, the transport device load generation amount 3500 of each transport device on each scheduled shipping date is multiplied by the transport device processing probability 2600 of each transport device on each processing date base shipping date (processing date-shipping date), and multiplied by the value. Therefore, the amount of generated load on the transport device having the same processing date s is totaled for each processing date s and for each transport device m, and the transport device load generated amount 3600 of each transport device m for each processing date s is obtained. Then, the conveyance device load generation amount 3600 of each conveyance device m for each processing day s is subtracted from the preset conveyance device capacity upper limit 900 for each conveyance device m, and each conveyance device m for each processing date s is subtracted. The net upper limit value 3700 of the transfer device capacity is obtained. Then, on the processing date s, the amount (weight) that the transport device m transports all the products belonging to the shipping group i is not less than or equal to the transport device capacity net upper limit 3700 of each transport device m for each processing date s. Develop a shipping plan under the restriction that it must not be. As described above, in the present embodiment, the upper limit value of the transport device, which is obtained by subtracting the processing amount for the confirmed order of each transport device on each processing day s, is set in the constraint equation. Therefore, for example, even when an order with a known shipping plan is included in the order information, such as when a product is transported on an export ship, a shipping plan can be created.

In the present embodiment, the case where the unconfirmed order information and the confirmed order information are separately input has been described as an example. However, this is not necessarily required. For example, all of the order information may be acquired, and the acquired order information may be divided into those for which the scheduled shipping date has been determined and those for which the scheduled shipping date has not been determined. Further, the order information in which the scheduled shipping date is not described, and the confirmed command information including the product number and the scheduled shipping date are input, and these information are combined to separate the unconfirmed order information and the confirmed order information. You may do it.
Also in this embodiment, the modifications shown in the first to third embodiments can be employed.

Each embodiment of the present invention described above can be realized by a computer executing a program. Further, a means for supplying the program to the computer, for example, a computer-readable recording medium such as a CD-ROM recording such a program, or a transmission medium for transmitting such a program may be applied as an embodiment of the present invention. it can. A program product such as a computer-readable recording medium that records the program can also be applied as an embodiment of the present invention. The programs, computer-readable recording media, transmission media, and program products are included in the scope of the present invention.
In addition, each of the embodiments of the present invention described above is merely an example of implementation in practicing the present invention, and the technical scope of the present invention should not be construed as being limited thereto. It will not be. That is, the present invention can be implemented in various forms without departing from the technical idea or the main features thereof.

DESCRIPTION OF SYMBOLS 100 Shipment planning apparatus 201 Order information acquisition part 202 Order information storage part 203 Conveyance result data acquisition part 204 Conveyance result data storage part 205 Shipment group definition logic acquisition part 206 Shipment group definition logic storage part 207 Shipment group discrimination | determination part 208 Shipment group discrimination | determination part 208 209 Transport device work probability calculation unit 210 Transport device work probability storage unit 211 Shipment resource matrix generation unit 212 Shipment resource matrix storage unit 213 Constraint condition acquisition unit 214 Constraint condition storage unit 215 Constraint expression setting unit 216 Planning policy acquisition unit 217 Planning policy Storage unit 218 Evaluation function setting unit 219 Optimization unit 220 Shipment plan output unit 3201 Confirmed order acquisition unit 3202 Confirmed order storage unit 3203 Shipment group determination unit 3204 Shipment group storage unit 3205 Confirmed order matrix generation unit 3 206 Confirmed order matrix storage unit 3207 Conveyance device work amount calculation unit for confirmed order

Claims (19)

A shipping planning device for planning a shipment amount of a plurality of products shipped from a warehouse using a plurality of transfer devices according to shipping date,
Order information acquisition means for acquiring order information including a shipment amount of the product and a shipping deadline date of the product;
A transfer device work occurrence probability acquisition means for acquiring a transfer device work occurrence probability that is the number of uses per unit product of the transfer device used to ship the product;
Shipment date indicating that the value obtained by multiplying the shipment amount of each product by the shipping equipment work occurrence probability related to the product is less than or equal to the transport device capacity upper limit set for each transport device and each shipment date A constraint equation setting means for setting another constraint equation;
A step function representing a target shipment amount of the product set based on the order information, which is 0 until the shipment date becomes the shipment deadline date, and becomes the target shipment amount when the shipment date becomes the shipment deadline date. The maximum load capacity of the transportation means that transports the product, and the value of the evaluation for the shipping deadline violation amount expressed by the difference between the value of the step function and the shipment quantity of the product, and the product loaded on the transportation means a weight coefficient representing a balance between the value of the evaluation for an empty payload represented by the difference between the load capacity, the value of the evaluation for the previous Kisora payload, the balance between the value of evaluation of the ship due date violations amount Weighting factor acquisition means for acquiring a weighting factor to be represented ;
Using the weighting coefficients obtained by the weighting factor obtaining means, the ship due date violations amount for the product, evaluation function represented by-out sum weighted with empty load of the transport means for transporting the products An evaluation function setting means for setting
Optimization means for performing optimization calculation by mixed integer programming that minimizes the value of the evaluation function within a range satisfying the constraint by the shipping date constraint equation;
As a result of the optimization calculation by the optimization means, information related to the shipping plan including the shipping amount by shipping date of the product when the value of the evaluation function is minimized within the range satisfying the constraint by the constraint equation A shipping plan drafting device comprising display means for displaying on a display device. The violating amount of the product due date is the target shipping amount in a period excluding the dead zone period that is a period in which the difference between the shipping amount and the target shipping amount is not considered from the planning period of the target shipping amount of the product The shipment plan planning apparatus according to claim 1, wherein the shipment plan amount is expressed by a difference between a shipment amount of the product and a shipment amount in a period excluding the dead zone period from the planning period. It further has a transport record acquisition means for acquiring a transport record of the transport device when the shipped product is shipped,
The said conveyance apparatus work occurrence probability acquisition means calculates the said conveyance apparatus work occurrence probability based on the conveyance performance of the said conveyance apparatus at the time of shipping the said shipped product. Shipping planning equipment. A shipping group generating means for grouping a plurality of products based on the order information, and generating a shipping group that is a collection of orders having the same product transportation form and destination ;
Shipping resource matrix generating means for generating a shipping resource matrix that represents a shipment amount of the product by each shipping group and by each shipping deadline date;
The transfer device work occurrence probability acquisition means acquires the transfer device work occurrence probability for each shipping group,
The constraint equation setting means adds a value obtained by multiplying the shipment amount of each product belonging to the shipment group by the shipment amount by the transport equipment work occurrence probability related to the shipment group to all the shipment groups. Set a shipping date constraint expression that indicates that the capacity is below the upper limit of the equipment capacity,
The shipment deadline violation amount is a step function that represents a target shipment amount of the product for each shipment group set based on the shipment funding matrix , and is 0 until the shipment date becomes the shipment deadline date, It is expressed by the difference between the value of the step function that becomes the target shipping amount when the shipping date is the shipping deadline date, and the shipping amount of the product by the shipping group,
The display means, as a result of the optimization calculation by the optimization means, when the value of the evaluation function is minimized within a range satisfying the constraint by the shipping date constraint formula, by shipping date of the product, shipping group The shipping plan planning device according to any one of claims 1 to 3, wherein information related to a shipping plan including another shipping amount is displayed on a display device. The weighting factor acquisition unit is configured to evaluate an evaluation value for the number of mutual products, which is the number of the shipping groups to which a product loaded on one transportation unit belongs, and an evaluation value for the shipping deadline date violation amount and the empty loading amount. Further acquiring a weighting factor representing a balance with the evaluation value ,
Instead of a weighting factor that represents a balance between an evaluation value for the shipping deadline violating amount and an evaluation value for the empty loading amount, an evaluation value for the empty loading amount is used as an evaluation value for the shipping deadline violation amount. A weighting factor representing the balance between the value and the value of the evaluation for the product number is used,
Instead of a weighting factor that represents a balance between an evaluation value for the empty load amount and an evaluation value for the shipment deadline date violation amount, an evaluation value for the empty load amount is evaluated with respect to the shipment deadline date violation amount. A weighting factor representing the balance between the value and the value of the evaluation for the product number is used,
The evaluation function setting means uses the weighting factor obtained by the weighting factor obtaining means to use the weighting factor in violation of the shipping deadline date, the empty loading amount of the transportation means for transporting the product, and the transportation means. shipment planning apparatus according to claim 4, characterized in that it sets an evaluation function expressed by-out weighted sum of the phase product number in. At each processing date standard shipment date for the total processing amount of the conveyance device at all processing date standard shipping dates set as the shipping date based on the processing date that is the implementation date of the conveying device work related to the product A transport device processing probability that is a ratio of the processing amount of the transport device, and further includes a transport device processing probability acquisition unit that acquires the processing date based on the shipment date and the transport device,
The constraint equation setting means includes a shipment amount of a product according to a shipping date expressed using the processing date reference shipping date, the transfer device work occurrence probability related to the transfer device, and the transfer device processing related to the transfer device. Processing day constraint indicating that the value obtained by multiplying the probability multiplied by all the set processing date reference shipping dates is less than or equal to the transport device capacity upper limit set for each processing date of the transport device 6. The shipping plan planning device according to claim 1, wherein the formula is set in place of the shipping date constraint formula. It further has a transport record acquisition means for acquiring a transport record of the transport device when shipping the product,
The transport device processing probability acquisition means calculates the transport device processing probability for each processing device based on a processing date based on a shipping date based on a transport performance of the transport device when shipping the product. The shipping schedule planning device according to claim 6. A shipping date information acquiring means for acquiring information on a shipping date that is confirmed for a part of the product;
Multiplying the shipment amount of the product for which the planned shipment date is determined by the planned shipment date by the probability of occurrence of the transfer device work, each conveyance for each planned shipment date in the product for which the planned shipment date is determined A load generation amount calculation means for each scheduled shipment date for calculating the load generation amount of the device;
In the product for which the scheduled shipping date is confirmed, the amount of load generated on each transport device by the scheduled shipping date is multiplied by the transport device processing probability of each transport device on each processing date reference shipping date, and the processing date is Load generation amount calculation means for each processing day for calculating the amount of load generation of each transport device for each processing day by summing the amount of load generation for each transport device that is the same by processing day and each transport device,
By subtracting the amount of load generated on each transport device for each processing day from the preset transport device capacity upper limit for each transport device and each processing date, the net transport device capacity for each transport device for each processing day An upper limit calculating means for calculating an upper limit,
The constraint equation setting means is a shipment amount of the product by shipment date expressed using the processing date reference shipment date, and the shipment amount by shipment date of the product for which the planned shipment date is not determined, A value obtained by multiplying the transport device work occurrence probability related to the transport device by the transport device processing probability related to the transport device for all the set processing date reference shipment dates is the value of the transport device. 8. The shipping plan drafting apparatus according to claim 6, wherein a constraint equation is set that indicates that the capacity is less than or equal to the net upper limit value of the transfer device capacity for each processing day. A group of a plurality of products included in the order information based on the order information of the product for which the scheduled shipping date is fixed and including the shipping amount of the product and the shipping deadline date of the product A second shipping group generation means for generating a shipping group by dividing;
A confirmed order matrix generating means for generating a confirmed order matrix for each shipment group, for each scheduled shipment date, and for representing a shipment quantity of a product for which the scheduled shipment date is confirmed;
The shipment generation amount calculation unit for each scheduled shipment date is multiplied by the shipping device work generation probability by the shipment amount for each shipment group and for each scheduled shipment date shown in the confirmed order matrix. Calculate the amount of load generated on each transport device for each shipment group and each scheduled shipment date for products for which the planned date is confirmed,
The processing day load generation amount calculation means is configured to calculate each processing date reference shipping date for each load generation amount of each transport device for each shipping group and each shipping scheduled date in the product for which the shipping scheduled date is determined. Multiply the transfer device processing probability of the transfer device, and calculate the load generation amount of the transfer device with the same processing date by processing day and transfer device, and calculate the load generation amount of each transfer device by processing date The shipping plan planning device according to claim 8, wherein: A shipping planning method for planning a shipment amount for each shipment date of a plurality of products shipped from a warehouse using a plurality of transfer devices,
An order information acquisition step for acquiring order information including a shipment amount of the product and a shipping deadline date of the product;
A transport device work occurrence probability acquisition step of acquiring a transport device work occurrence probability that is the number of times of use per unit product of the transport device used to ship the product;
Shipment date indicating that the value obtained by multiplying the shipment amount of each product by the shipping equipment work occurrence probability related to the product is less than or equal to the transport device capacity upper limit set for each transport device and each shipment date A constraint equation setting step for setting another constraint equation;
A step function representing a target shipment amount of the product set based on the order information, which is 0 until the shipment date becomes the shipment deadline date, and becomes the target shipment amount when the shipment date becomes the shipment deadline date. The maximum load capacity of the transportation means that transports the product, and the value of the evaluation for the shipping deadline violation amount expressed by the difference between the value of the step function and the shipment quantity of the product, and the product loaded on the transportation means a weight coefficient representing a balance between the value of the evaluation for an empty payload represented by the difference between the load capacity, the value of the evaluation for the previous Kisora payload, the balance between the value of evaluation of the ship due date violations amount A weighting factor obtaining step for obtaining a weighting factor to be represented ;
Using the weighting coefficients obtained by the weighting factor acquisition step, and the ship due date violations amount for the product, evaluation function represented by-out sum weighted with empty load of the transport means for transporting the products An evaluation function setting step for setting
An optimization step of performing optimization calculation by mixed integer programming that minimizes the value of the evaluation function within a range satisfying the constraint by the shipping date constraint equation;
As a result of the optimization calculation in the optimization step, information related to a shipping plan including a shipping amount for each shipping date of the product when the value of the evaluation function is minimized within a range satisfying the constraint by the constraint equation And a display step of displaying on the display device. The violating amount of the product due date is the target shipping amount in a period excluding the dead zone period that is a period in which the difference between the shipping amount and the target shipping amount is not considered from the planning period of the target shipping amount of the product The shipping plan planning method according to claim 10, wherein the shipping quantity is expressed by a difference between a shipping quantity of the product and a shipping quantity in a period excluding the dead band period from the planning period. It further has a transport record acquisition step for acquiring a transport record of the transport device when the shipped product is shipped,
The said conveyance apparatus work occurrence probability acquisition step calculates the said conveyance apparatus work occurrence probability based on the conveyance performance of the said conveyance apparatus at the time of shipping the said shipped product. Shipping planning method. A shipping group generation step of grouping a plurality of products based on the order information to generate a shipping group that is a collection of orders with the same product transport form and destination ;
A shipping resource matrix generating step for generating a shipping resource matrix that represents a shipment amount of the product by each shipping group and by each shipping due date;
The transport equipment work occurrence probability acquisition step obtains the transport equipment work occurrence probability for each shipping group,
In the constraint formula setting step, a value obtained by multiplying a shipment amount for each shipment date of a product belonging to the shipping group by the transport equipment work occurrence probability related to the shipping group is added to all the shipping groups. Set a shipping date constraint expression that indicates that the capacity is below the upper limit of the equipment capacity,
The shipment deadline violation amount is a step function that represents a target shipment amount of the product for each shipment group set based on the shipment funding matrix , and is 0 until the shipment date becomes the shipment deadline date, It is expressed by the difference between the value of the step function that becomes the target shipping amount when the shipping date is the shipping deadline date, and the shipping amount of the product by the shipping group,
In the display step, as a result of the optimization calculation in the optimization step, when the value of the evaluation function is minimized within the range satisfying the constraint by the shipping date constraint equation, the product by shipping date, the shipping group The shipping plan planning method according to any one of claims 10 to 12, wherein information related to a shipping plan including another shipping amount is displayed on a display device. In the weighting factor acquisition step, an evaluation value for the number of mutual products, which is the number of the shipping groups to which the product loaded on one transportation means belongs, an evaluation value for the shipping deadline violation amount and the empty loading amount Further acquiring a weighting factor representing a balance with the evaluation value ,
Instead of a weighting factor that represents a balance between an evaluation value for the shipping deadline violating amount and an evaluation value for the empty loading amount, an evaluation value for the empty loading amount is used as an evaluation value for the shipping deadline violation amount. A weighting factor representing the balance between the value and the value of the evaluation for the product number is used,
Instead of a weighting factor that represents a balance between an evaluation value for the empty load amount and an evaluation value for the shipment deadline date violation amount, an evaluation value for the empty load amount is evaluated with respect to the shipment deadline date violation amount. A weighting factor representing the balance between the value and the value of the evaluation for the product number is used,
The evaluation function setting step uses the weighting factor acquired in the weighting factor acquisition step to use the weighting factor violation amount for the product, an empty loading amount of the transportation means for transporting the product, and the transportation means. shipment planning method according to claim 13, characterized in that sets an evaluation function expressed by-out weighted sum of the phase product number in. At each processing date standard shipment date for the total processing amount of the conveyance device at all processing date standard shipping dates set as the shipping date based on the processing date that is the implementation date of the conveying device work related to the product A transport device processing probability acquisition step of acquiring the transport device processing probability, which is a ratio of the processing amount of the transport device, by processing date reference shipment date, by transport device;
The constraint equation setting step includes a shipment amount of a product according to a shipping date expressed using the processing date reference shipping date, the transfer device work occurrence probability related to the transfer device, and the transfer device processing related to the transfer device. Processing day constraint indicating that the value obtained by multiplying the probability multiplied by all the set processing date reference shipping dates is less than or equal to the transport device capacity upper limit set for each processing date of the transport device 15. The shipping plan planning method according to any one of claims 10 to 14, wherein the formula is set instead of a shipping date-specific constraint formula. It further includes a conveyance result acquisition step of acquiring a conveyance result of the transfer device when shipping the product,
In the transport device processing probability acquisition step, the transport device processing probability is calculated for each processing device based on a processing date based on a shipping date based on a transport performance of the transport device when the product is shipped. The shipping plan planning method according to claim 15. A planned shipping date information acquisition step for acquiring planned shipping date information for a part of the product;
Multiplying the shipment amount of the product for which the planned shipment date is determined by the planned shipment date by the probability of occurrence of the transfer device work, each conveyance for each planned shipment date in the product for which the planned shipment date is determined A load generation amount calculation step for each scheduled shipping date for calculating the generation amount of the load on the device;
In the product for which the scheduled shipping date is confirmed, the amount of load generated on each transport device by the scheduled shipping date is multiplied by the transport device processing probability of each transport device on each processing date reference shipping date, and the processing date is A load generation amount calculation step for each processing day that calculates the generation amount of the load of each conveyance device for each processing day by calculating the generation amount of the load of each conveyance device for each processing device by the processing date,
By subtracting the amount of load generated on each transport device for each processing day from the preset transport device capacity upper limit for each transport device and each processing date, the net transport device capacity for each transport device for each processing day An upper limit calculating step for calculating an upper limit,
The constraint equation setting step is a shipment amount of the product by shipment date expressed using the processing date reference shipment date, and the shipment amount by shipment date of the product for which the planned shipment date is not determined, A value obtained by multiplying the transport device work occurrence probability related to the transport device by the transport device processing probability related to the transport device for all the set processing date reference shipment dates is the value of the transport device. The shipping plan planning method according to claim 15 or 16, wherein a constraint equation is set that indicates that the transfer device capacity is less than or equal to the net upper limit value for each processing day. A group of a plurality of products included in the order information based on the order information of the product for which the scheduled shipping date is fixed and including the shipping amount of the product and the shipping deadline date of the product A second shipping group generation step for generating a shipping group by dividing;
A finalized order matrix generating step for generating a finalized order matrix for each shipping group, for each planned shipping date, and for generating a finalized order matrix that represents a shipping amount of a product for which the planned shipping date is fixed,
The shipment generation amount calculation step for each scheduled shipment date is obtained by multiplying the shipment amount for each shipment group and each scheduled shipment date shown in the confirmed order matrix by the transfer equipment work occurrence probability. Calculate the amount of load generated on each transport device for each shipment group and each scheduled shipment date for products for which the planned date is confirmed,
The processing day load generation amount calculating step includes the processing date reference shipment date for each generation amount of load on each transport device for each shipment group and each scheduled shipment date in the product for which the scheduled shipment date is determined. Multiply the transfer device processing probability of the transfer device, and calculate the load generation amount of the transfer device with the same processing date by processing day and transfer device, and calculate the load generation amount of each transfer device by processing date The shipping plan planning method according to claim 17, wherein: A computer program for causing a computer to plan a shipment amount for each shipment date of a plurality of products shipped from a warehouse using a plurality of transfer devices,
An order information acquisition step for acquiring order information including a shipment amount of the product and a shipping deadline date of the product;
A transport device work occurrence probability acquisition step of acquiring a transport device work occurrence probability that is the number of times of use per unit product of the transport device used to ship the product;
Shipment date indicating that the value obtained by multiplying the shipment amount of each product by the shipping equipment work occurrence probability related to the product is less than or equal to the transport device capacity upper limit set for each transport device and each shipment date A constraint equation setting step for setting another constraint equation;
A step function representing a target shipment amount of the product set based on the order information, which is 0 until the shipment date becomes the shipment deadline date, and becomes the target shipment amount when the shipment date becomes the shipment deadline date. The maximum load capacity of the transportation means that transports the product, and the value of the evaluation for the shipping deadline violation amount expressed by the difference between the value of the step function and the shipment quantity of the product, and the product loaded on the transportation means a weight coefficient representing a balance between the value of the evaluation for an empty payload represented by the difference between the load capacity, the value of the evaluation for the previous Kisora payload, the balance between the value of evaluation of the ship due date violations amount A weighting factor obtaining step for obtaining a weighting factor to be represented ;
Using the weighting coefficients obtained by the weighting factor acquisition step, and the ship due date violations amount for the product, evaluation function represented by-out sum weighted with empty load of the transport means for transporting the products An evaluation function setting step for setting
An optimization step of performing optimization calculation by mixed integer programming that minimizes the value of the evaluation function within a range satisfying the constraint by the shipping date constraint equation;
As a result of the optimization calculation in the optimization step, information related to a shipping plan including a shipping amount for each shipping date of the product when the value of the evaluation function is minimized within a range satisfying the constraint by the constraint equation A computer program that causes a computer to execute a display step for performing processing for display on a display device.

Images